Number,Title,Abstract,Body 1,A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays," At the intersection of technology and marketing, this study develops a framework to unobtrusively detect salespeople's faces and simultaneously extract six emotions: happiness, sadness, surprise, anger, fear, and disgust. The authors analyze 99,451 sales pitches on a livestream retailing platform and match them with actual sales transactions. Results reveal that each emotional display, including happiness, uniformly exhibits a negative U-shaped effect on sales over time. The maximum sales resistance appears in the middle rather than at the beginning or end of sales pitches. Taken together, the results show that in one-to-many screen-mediated communications, salespeople should sell with a straight face. In addition, the authors derive closed-form formulae for the optimal allocation of the presence of a face and emotional displays over the presentation span. In contrast to the U-shaped effects, the optimal face presence wanes at the start, gradually builds to a crescendo, and eventually ebbs. Finally, the study shows how to objectively rank salespeople and circumvent biases in performance appraisals, thereby making novel contributions to people analytics. This research integrates new types of data and methods, key theoretical insights, and important managerial implications to inform the expanding opportunity that livestream e-commerce presents to marketers to create, communicate, deliver, and capture value.","Livestream retailing augments traditional go-to-market strategies by reaching consumers via screen-mediated sales presentations for a variety of products. Amazon Live, Facebook Live, Taobao Live, and QVC serve as prominent exemplars. This type of retailing blends technology and marketing: the technology integrates video stream broadcast platforms, electronic payment systems, and forward and reverse logistics for efficient delivery and hassle-free returns, and the marketing combines entertainment and retailing, enhances reach via influencer marketing, shortens the purchase journey to the duration of the sales presentation, and permits value capture via innovative payment plans. In a typical video sales pitch, a host (salesperson) nudges a prospective customer through the purchase funnel by building awareness of an item's features, benefits, price, and discounts, as well as instilling urgency to buy. For example, on Taobao Live, which reaches over 37 million Chinese viewers monthly, top influencer Wei Ya helped Procter & Gamble accelerate its customers' journey from awareness to purchase and Tesla generate customer leads ([19]).The continuous stream of sales presentation videos can be captured using advanced computing capabilities ([40]; [59]). Using video footage, marketing scientists can apply artificial intelligence technologies to automatically detect a salesperson's face in each frame, extract emotional expressions, and relate them to actual customers' behavioral data —all unobtrusively at a large scale— to generate novel insights ([34]), thereby augmenting the sparse knowledge on the business impact of emotional displays.Recent studies have investigated the effects of emotional displays on various marketing metrics. For example, [50] offer the first study to automatically extract the emotions of joy and surprise that viewers experience when watching television commercials and then relate these emotions to attention and ad avoidance behavior. Similarly, [32] examine the impact of emotional displays on sales using the Facial Action Coding System ([13]) to categorize a set of emotions (e.g., happiness, surprise, disgust) based on facial expressions of viewers watching movie trailers, compute the average watching intention for each movie trailer, and relate it to box-office revenues. They find that viewers' emotional displays of happiness positively influence both watching intentions and box-office revenues. However, although [32] incorporate prospective consumers' emotional displays into their study, the role of emotional displays on the seller side of the exchange dyad remains an unexplored cue that shapes consumers' purchases. Indeed, in their survey of the literature on emotions, [ 3], p. 184) emphasize that ""much of what we do know is confined to consumer behavior, as opposed to the behavior of salespeople or marketing managers.""To our knowledge, the current study is the first to assess the sales impact of product, price, sales force, advertising, and promotion in the presence of a salesperson's face and emotional displays. Specifically, we address the following knowledge gaps: Do salespeople's emotions in livestream presentations impact sales? If so, to what extent? How do the effects of emotions vary over an item's presentation span? What is the optimal allocation of face presence and emotions over the presentation span?To answer these and related questions, we collaborate with a livestream retailer that broadcasts television shows 24 hours each day of the week, deploys salespeople to deliver live sales presentations of hedonic products, receives payments by credit cards, and ships orders by mail. A typical show lasts one hour and presents about eight items. We analyze the video data consisting of 62.32 million frames over two years. To put this scale in perspective, this footage exceeds two million 30-second TV ads. Then, we apply two machine-learning algorithms: real-time face detection ([56]) and real-time emotion classification ([ 1]). Specifically, the face detection algorithm discovers the presence or absence of a face in every frame of the video, while the emotions classifier (based on a convolutional neural network with rectified linear unit activation) assigns probabilities to the facial expressions in each frame with a face. Thus, we unobtrusively extract the display of emotions of each salesperson in one-to-many screen-mediated marketing communications in consumer markets.Next, across 99,451 sales pitches, we match the salespeople's six emotional displays —happiness, sadness, surprise, anger, fear, and disgust— to how long each item was shown, the product category to which it belongs, the number of units sold, the price charged, and whether shipping fees were waived. Finally, we extend marketing mix models on two frontiers: the inclusion of emotional displays and salespeople's effectiveness. We emphasize that the literature on marketing mix models is vast, as is the literature on emotions; however, they do not overlap. This study bridges the two distinct domains.Our analysis of large-scale video data shows that salespeople's emotions negatively impact sales across all six emotions, including happiness. The magnitude of sales decline across all the emotions is.47%, which is more than double the free-shipping effect (.20%). Happiness constitutes more than one-third of the total sales decline. Thus, we uncover a new maxim: sell with a straight face (i.e., reduce facial expressions).Furthermore, the level of the optimal face presence reduces over the initial 10% span, then gradually increases as the presentation progresses, and subsequently tapers down in the last 15% span. Finally, most marketing mix models ignore the role of the sales force (e.g., [ 2]; [39]), and when they do include it (e.g., [16]), the sales force variable is operationalized at an aggregate level (e.g., number of salespeople, number of calls). Consequently, companies are limited in their insights into an individual salesperson's effectiveness. By contrast, the proposed framework uses person-specific dummy variables to estimate individual salesperson's impact, yielding valuable information on salespeople's performance, which circumvents managers' cognitive biases (e.g., homophily) in recognizing excellence and identifying candidates for retraining, thus contributing to people analytics in a sales setting. Next, we describe the conceptual background needed to interpret the empirical results. Conceptual BackgroundFacial expressions are social displays that a sender strategically deploys to elicit a desired response from a receiver ([11]; [15]). In other words, facial expressions are ""declarations that signify our trajectory in a given social interaction, that is ... what we would like the other to do"" ([15], p. 130). They are social communicative moves that serve as ""tools for social influence"" ([11], p. 393).A sender may mask true intentions. The onus is, therefore, on the receiver to decipher the sender's intent. The Emotions as Social Information (EASI) model ([51], [52]; [53]) asserts that buyers scrutinize the seller's expressions in commercial exchanges in an attempt to discern the seller's strategic intentions. For example, [57] show that sellers sporting a broad smile during an encounter are perceived as less competent and that perceived incompetence is more likely to be evident among prevention-focused customers and in high-risk consumption settings. Furthermore, their field study in a crowdfunding context reveals that a project creator with a broad smile is perceived as less competent, which reduces the total amount of money pledged for a project, the total number of large-scale donations made by backers, and the average amount of money pledged per backer. Similarly, [10] find that displays of intense happiness (e.g., a broad smile) by customer-facing employees can undermine trust and reduce satisfaction with the product.[44], [45], [46]) provides a theoretical explanation for why receivers are likely to draw certain inferences with respect to a sender's facial expression, and the EASI model offers clues on how receivers are likely to react to the cue. Salespeople's expressions elicit customers' inferences about sellers' characteristics such as competence, trustworthiness, and persuasive intent. Such inferences, in turn, impact customers' purchasing behaviors. Drawing on extant theory, Table 1 presents the seller's facial expressions, intent, consumers' inferences about sellers, and consumers' action tendency with examples. According to [53], consumers' action tendencies are to ( 1) move toward (i.e., consumers experience a positive emotional reaction toward the influence attempt and thereby seek to cooperate with the seller); ( 2) move away (i.e., consumers experience a slightly negative emotional reaction toward the influence attempt and thereby seek to temporarily ignore or avoid the sender); or ( 3) move against (i.e., consumers experience a highly negative emotional reaction toward the influence attempt and thereby seek to terminate the interaction). Thus, Table 1 explains how salespeople's emotional displays trigger buyers' inferences in one-to-many broadcast communications.GraphTable 1. The Implications of a Sender's Strategic Social Communicative Moves. 1 a[11].2 b[53].More specifically, Table 1 provides theoretical bases to interpret our empirical results. First, positive facial expression (i.e., happiness) invokes the action tendency to move against. In a competitive buyer–seller exchange setting, a seller's happiness expression engenders consumers' inference that the seller is gaining an advantage, thereby reducing the seller's trustworthiness as well as consumers' purchasing tendency ([51], [52]; [53]) — a finding consistent with [57] that a seller's broad smile results in the inference of low competence and the action tendency of reduced buying activity. Second, negative facial expressions (i.e., sadness, anger, fear, and disgust) invoke the action tendency to move away. For instance, a seller's sadness expression in a selling context invokes the consumers' inference of garnering empathy as an attempt to lower their guard, which can be off-putting to consumers, who might then either ignore or avoid the seller. Consistent with this expectation, [10]) show that the display of sadness by frontline employees undermines trust and lowers satisfaction. Last, surprise can be either a positive or negative facial expression: consumers can infer that the seller is trying to garner attention, which invokes the action tendency to either move toward or move against, depending on whether consumers believe the expression is appropriate. Livestream Retail Analytics FrameworkThe proposed framework circumvents limitations such as simulated interactions in laboratory studies, survey-based studies relying on self-reports, manual observation and coding of facial displays, small sample sizes, limited set of emotional displays, and lack of business metrics as the response variable. For example, [25] investigate the impact of service representatives' happiness expressions on subjects in lab settings (using trained student actors). [17] conduct surveys with airline passengers' assessments of flight attendants, and [27] rely on surveys completed by sales managers self-reporting their own ability to perceive their salespeople's emotions. [42] manually evaluates a small sample of bank employees' smiles in salesperson–customer service encounters. These studies lack the full spectrum of emotional displays and sales performance as the response. [26], a notable exception, seek to understand the content effects on sales. Specifically, they analyze a small sample of 275 sales pitches from Home Shopping Network and incorporate minute-by-minute cumulative sales; however, they ignore the role of facial expressions. To circumvent the aforementioned limitations, we develop a framework that unobtrusively collects nonsimulated market interactions, does not rely on self-reports, does not require manual observation or coding of facial displays, involves a large sample size, covers a broad spectrum of six emotional displays, and, most importantly, uses sales transactions as the response variable. Figure 1 presents the ten-step framework to capture and analyze the structured and unstructured data from livestream retailing.Graph: Figure 1. Livestream retail analytics framework. Data CaptureThe first two pennants in Figure 1 list three steps that pertain to data capture. Transaction data contain structured information such as quantity of items sold, prices of items, duration of display, shipping cost, and product category. Video footage of salespeople's presentations offers the unstructured data. We process the video footage as follows. Each video frame is a colored image with a resolution of 480 × 360 pixels. For every second of the video footage, we select a frame, convert it to grayscale, and present it to a pretrained OpenCV frontal face detection model based on the Haar cascade algorithm ([56]). For each detected face, the grayscale frame bounded by the face's region of interest is forwarded to an emotion classification model to infer the emotional state of the salesperson by producing probabilities for happiness, sadness, surprise, anger, fear, and disgust. We classify emotional displays using a pretrained mini-Xception model developed by [ 1]). Thus, we unobtrusively extract data on whether a face exists in 62.32 million frames and the probabilities of emotional displays. The third pennant in Figure 1 uses time stamps (i.e., the start and end times of item displays) to compute the display duration. We match 25,565 distinct items across 6,065 shows to the accounting data on orders placed, selling prices, and free shipping waivers. Analytics PipelineThe fourth pennant in Figure 1 consists of dynamic time warping (step 4), dimension reduction (step 5), feature engineering (step 6), and mixed models (step 7). To understand dynamic time warping, let Eisk(t) denote the time pattern of the emotional display k, where k=1,...,6 for an item i in show s. Data analysis commonly uses variable transformation such as squaring, which alters the magnitude of Eisk(t) but keeps its x values (time) unaltered. In contrast, we transform Eisk(t) by shifting, stretching, or shrinking the time argument, denoted by t in Eisk(t) , but keeping its magnitude (y values) the same. This shifting, stretching, or shrinking applies to each item i in show s and emotion k. For example, Sin(t) and Cos(t) are different curves, yet if we replace t in Cos(t) by ( π2−t) , we shift the cosine curve along the time dimension to overlap it with the sine curve exactly. The function ϕ(t)=π2−t is called a ""warping"" function that performs shifting; however, more generally, it can stretch or shrink time, differentially at various instants, to align curves more closely with each other with respect to their landmarks such as peaks, valleys, and inflection points. This process of dynamic time warping is also known as curve registration or landmark alignment. The resulting aligned curves serve as inputs for analysis rather than the raw curves Eisk(t) . Subsequently, we present the empirical results with and without curve alignment to understand the benefits of this optional step.Dimension reduction enables us to capture the dynamic effects of emotional displays on quantity sold. Specifically, for each item-show, the time t in Eisk(t) spans over 30 epochs, which are defined as 1/30th of the total duration of item i displayed in show s. Consequently, we have 180 additional variables (i.e., 30 epochs for six emotions). Furthermore, we generate an additional 180 variables by including its quadratic terms and yet another 360 variables by interacting them with price and promotion. To maintain parsimony and mitigate collinearity, we extract the principal component to capture ""happiness"" (say, when k=1 ) via the principal scores zkis=∑t=130e^ktEisk(t) , where e^k=(e^k,1,e^k,2,...,e^k,30)′ is the principal eigenvector that reduces the dimensionality from 30 epochs to the scalar zkis . Then, we regress the quantity sold Qis on the principal scores zkis to estimate the trajectory of sales impact of an emotional display together with its confidence intervals. For details, see the Web Appendix.Feature engineering supplements the new features to represent the quadratic and interaction effects. To this end, we included zkis2 for each emotion k and interaction terms such as zkis×Xis , where Xis denotes a moderating variable of interest (e.g., price). To generate the outputs listed in the fifth pennant in Figure 1, we formulate a set of mixed models. Model DevelopmentFigure 2 illustrates how marketing mix — product, price, sales force, display duration (advertising), and free shipping (promotion) — together with face presence and emotional displays (happiness, sadness, surprise, anger, fear, and disgust) affect the focal outcome representing customers' purchase behavior (i.e., sales). In Model 1, we formulate the marketing mix model with marketing mix variables, time effects, and random effects for items and shows. Model 2 extends Model 1 by incorporating the face presence and six emotional displays. Model 3 adds the quadratic effects of emotional displays. Model 4 further augments Model 3 with interactions of price and promotion with the emotional displays.Graph: Figure 2. Modeling framework. Model 1: Incorporating Salespeople and Time Effects in Marketing Mix ModelsWe investigate how the number of units of an item sold on a given show varies with the item's price, its duration of display, whether free shipping was waived, the product category to which it belongs, the salesperson who presented it, and the time effects (day effect, week effect, and year). The model specification is as follows: Ln(Qis)=β1Ln(Pis)+β2Ln(Dis)+β3Sis+β4Cis+∑j=122λjHjs+τ′Tis+μ0+μ1i+μ2s+ϵis, Graph( 1)where Qis denotes the quantity sold of an item i in the show s with ( i,s)=1,⋯,N=99,451 ; Pis represents the item's price in dollars; Dis is the display duration in seconds; Sis captures free shipping ( Sis=1) or not (Sis=0) ; and Cis={0,1} indicate one of the two types of products (whose names are not disclosed for confidentiality). These five variables represent the proxies for the traditional marketing mix variables: product, price, sales force, marketing communications (i.e., length of ad), and promotion (i.e., free shipping). Given the log-log specification, the parameters ( β1,β2) respectively yield the price and duration elasticity, which quantifies the percentage sales impact associated with a 1% increase in price or duration. The parameters ( β3,β4) measure the percentage change in sales due to free shipping and product category. In addition, Hs is a 22×1 dummy vector, with unity for the element j and zero elsewhere, that identifies individual salesperson j hosting the show s. The corresponding λj furnishes the sales lift due to various salespeople, j=1,...,22 , relative to the baseline salesperson 23. A single salesperson owns the entire show in our data. The parameters (μ0,μ1i,μ2s,ϵis) represent the fixed intercept, random intercept for items, random intercept for shows, and the usual zero mean and constant variance normal error term, respectively. The random effects parsimoniously reflect the variability about the intercept μ0 due to heterogeneous impact of items and shows.Time flows across 62.32 million seconds of the video footage in our analysis, and it exhibits periodicity for the seconds across days and for the days across weeks. To clarify, consider time in seconds since midnight. A total of 86,400 seconds elapse by the midnight of the next day, and then the clock resets to zero (00:00:00 hours). At 23:59:59 hours, the elapsed time is 86,399 seconds, and it is 5 seconds at 00:00:05 hour. Although the instants 23:59:59 and 00:00:05 differ by just 6 seconds, these two instants would be represented as if they are 86,394 seconds apart under a linear scale. Thus, to account for periodicity of the days and weeks, sine and cosine terms should be used as follows. Let tis represent the seconds of a day when an item i in show s is displayed, where the full day of 86,400 seconds equals 360˚ or 2π radians. Then the two periodic regressors for the day effect are Dis=(Sin[2πtis86400],Cos[2πtis86400])′ . Similarly, let dis represent the day of a week when an item i in show s is displayed, where the full week equals seven days. Then the two periodic regressors for the week effect are Wis=(Sin[2πdis7],Cos[2πdis7])′ . Because calendar years are not periodic, unlike seconds or days, let the dummy variable Yis indicate the years. Thus, Tis=(Yis,Wis′,Dis′)′ in Equation 1 includes these five regressors with the conformable parameter vector τ that constitutes the year effect, the week effect, and the day effect on item sales. Model 2: Incorporating the Presence of Face and Emotional DisplaysThe proposed livestream retail analytics framework provides the fraction of the frames containing a face when item i was displayed in show s, which we denote by Fis . In addition, when item i was displayed in show s, it furnishes the principal score zkis for happiness ( z1is ), sadness ( z2is ), surprise (z3is ), anger (z4is ), fear (z5is ), and disgust ( z6is ). Incorporating them in Model 2, we extend the right-hand side (RHS) of Model 1 as follows: Ln(Qis)=α0Fis+α1z1is+α2z2is+α3z3is+α4z4is+α5z5is+α6z6is+RHS(Model1), Graph( 2)where αk are the effects of face presence and six emotional displays on sales. Because the score zkis=∑t=130e^ktEisk(t) , the sales impact α^ke^kt exhibits the trajectory over t=1,...,30 epochs. Model 3: Incorporating Quadratic EffectsThe effects of emotional displays may wax and wane. For example, moderate happiness may be effective, but limited or excessive happiness display may not be. To investigate such intensity effects, we extend Model 2 by incorporating the quadratic effects of facetime and emotional displays. Then, Model 3 is given by Ln(Qis)=α0Fis+γ0Fis2+∑k=16αkzkis+∑k=16γkzkis2+RHS(Model1), Graph( 3)where zkis=∑t=130e^ktEisk(t) , and γk represent the quadratic effects for the face presence and emotional displays, respectively. Equation 3 also includes the simple effects of face presence ( α0) and emotions ( αk) , the marketing mix effects, and salesperson's effectiveness, time, and fixed and random intercepts via Model 1. Optimal allocation of face and emotionsWe derive the optimal face presence and emotional displays over time in the Web Appendix, which shows that the optimal number of frames to devote to face presence and each emotion k in every epoch t is given by Ft*={−α0e0t2γ0ifα0e0t>0,γ0<0,0otherwise.andEkt*={−αkekt2γkifαkekt>0,γk<0,0otherwise. Graph( 4)Thus, for every epoch t in the presentation span T, the optimal allocation of face presence is Ft*∑tFt*=e0t∑t=1Te0t ; and the optimal allocation for each emotional display k is Ekt*∑tEkt*=ekt∑t=1Tekt . To gain intuition, observe that Ft*∝e0t and Et*∝ekt in Equation 4, which reveals that face and emotions allocation are proportional to the eigenvector weights: the larger the weight, the greater the intensity of face presence or emotional expressions. Model 4: Incorporating Interaction EffectsVarious factors can moderate the impact of sellers' affective displays on customers' attitudinal and behavioral outcomes. For example, some studies investigate boundary conditions from perceivers' characteristics, such as emotional receptivity ([30]) and epistemic motivation ([57]). Others examine the moderating roles of the selling context, such as store busyness (e.g., [18]). To complement, we explore the moderating role of factors under managers' control such as price and promotion. Specifically, we augment Model 3 as follows: Ln(Qis)=∑k=16δkzkisPis+∑k=16ωkzkisSis+RHS(Model3). Graph( 5)In Equation 5, the free shipping effect equals β3+ωkzk, which depends on the level of emotional display, zk . Similarly, each emotion zk moderates the price elasticity ηk=β1+δkzkP . The preceding discussion completes the inclusion of emotions in marketing mix models. Empirical Analysis ContextA livestream retailer, whose identity remains confidential, broadcasts shows 24 hours a day, seven days a week, on its own television channel and sells exclusive hedonic products in multiple product categories. The salesperson hosting the show presents information on products and encourages viewers to place orders by telephone. Each show lasts for 60 or 120 minutes, is planned weeks in advance, and contains live sales pitches of items (i.e., not scripted or prerecorded). Besides selling, the salespeople attempt to build parasocial relationships with viewers so that they feel a bond with virtual personalities analogous to those with television celebrities or news anchors ([49]). DataOur direct-to-consumer retailer sells items from two product categories using 23 hosts as salespeople. The salesperson pitches multiple items during a show, and the item appears throughout the presentation span. We observe 99,451 sales pitches at an item-show level on salespeople's presence of face, their facial expressions, item prices, duration of display, shipping fee waivers, and, most importantly, actual sales as the dependent variable. Table 2 presents the descriptive statistics, and Tables 3 and 4 contain the estimation results for Models 1–4 obtained via the R package lme4.GraphTable 2. Descriptive Statistics. GraphTable 3. Sales Impact of Marketing Mix, Sales Force, and Time Effects. GraphTable 4. Sales Impact of Face and Emotional Displays. Results Sales impact of face presence and emotional displays Face presence[31]) apply convolutional neural networks to detect the presence of a person's face in a Kickstarter crowdfunding video and show empirically that the presence of a human face makes a difference in shaping the desired funding outcomes. But does it impact sales? If so, to what extent? Our study answers these questions. The estimate of.338 in Table 4 (Model 2) means that sales increase by.34% when a face is present, an effect common to all the hosts. For a specific salesperson, say salesperson 15, the impact of sales pitch is (.338+.418)=.756 , which means sales increase by.76%. This magnitude explains why the livestream retailer prefers live broadcasts even when items could have been posted on the internet in a faceless manner. Emotional displaysTable 4 for Model 2 partially presents the sales impact of happiness, sadness, anger, fear, and disgust. The estimates α^k for happiness (–.033), sadness (–.003), surprise (–.001), anger (–.033), fear (–.005), and disgust (–.012) are uniformly negative and statistically significant for all emotions except surprise. Thus, we conclude that emotional displays decrease sales.We present the dynamic pattern of emotional displays with (see Figure 3) and without (see Figure 4) dynamic time warping. These dynamic patterns emerge from the elements of the eigenvector e^kt across the 30 epochs t=1,...,30 . For clarity, Figures 3 and 4 present the epochs on the unit interval. The elements of the eigenvector e^kt , together with the estimates α^k , yield the total sales impact of emotions given by a^kt=α^ke^kt . Summing across all the epochs, the sales impact of emotional displays are as follows: happiness (–.18%), sadness (–.02%), surprise (–.004%), anger (–.18%), fear (–.03%), and disgust (–.06%). Happiness and anger induce the largest sales decline; surprise the smallest. Summing across these emotions, the magnitude of total sales decline (.47%) is more than twice the free-shipping effect (.198%). Happiness contributes more than one-third to the total sales decline.Graph: Figure 3. Time-varying sales impact of emotional displays with dynamic time warping.Graph: Figure 4. Time-varying sales impact of emotional displays without dynamic time warping.What accounts for the negative sales impact? As discussed in the ""Conceptual Background"" section, sellers' emotional displays trigger buyers' inference and action tendencies. Specifically, Table 1 shows that positive facial expressions such as happiness negatively impact sales because consumers infer that the seller is gaining an advantage at their expense, thereby reducing sellers' trustworthiness and, in turn, buyers' tendency to purchase ([51], [52]; [53]). This expectation corroborates [57]) findings, which show that a seller's broad smile results in a buyer's inference of a seller's low competence and reduces buying activity. A similar situation occurs with politicians sporting a ""permasmile"" (i.e., maintaining a smile for an extended period of time); they are not perceived as genuine, which induces distrust and leads to lost votes ([60]). As for negative facial expressions (i.e., sadness, anger, fear, and disgust), they invoke the action tendency to move away, which corroborates [10]) finding that frontline employees' displays of sadness undermine trust and reduce satisfaction. Last, surprise can be either positive or negative, and it results in an insignificant effect on sales. Thus, this large-scale evidence supports recent studies ([10]; [57]), and so we caution that emotional displays are bad for livestream retailing business.Over an item's presentation span, the magnitude of sales impact a^kt builds up, attains a maximum in the middle, and recedes toward the end. Across the six emotions, this dynamic pattern holds uniformly. Are the U-shaped patterns significant? Using the expressions in the Web Appendix, we plot the confidence intervals in Figures 3 and 4. We conclude that because zero does not belong in it, except for surprise, the rest of the emotional displays, including happiness, exert significantly negative effects on sales.What accounts for the U-shaped dynamics? The literature on advertising repetition (e.g., [ 5]; [ 9]; [38]; [41]) provides a plausible interpretation. As the sales pitch progresses, the repetitiveness of facial expressions exacerbates the negative sales impact (i.e., becomes more negative) and drives it to the lowest level. After that, often due to the tedium ([ 5]) of a protracted sales pitch, viewers' attention drifts from the message-related thoughts to their own thoughts ([ 9]) of purchase consideration, namely, balancing the benefits and costs of the presented item and deciding whether to buy. Consequently, the negative effect ameliorates during purchase consideration. [38] find a similar U-shaped pattern for the effectiveness of television commercials (see their Figures 4 and 9 for chocolate and cereal brands, respectively).Graph: Figure 9. Optimal face allocation. Quadratic effectsModel 3 specifies the quadratic effects to explore whether emotional displays can be optimized. Table 4 shows that the conditions α^k>0 and γ^k<0 are not satisfied by happiness, sadness, anger, fear, and disgust. Consequently, their resulting optimal level Ekt*=0 according to Equation 4. Although surprise satisfies the conditions α^3>0 and γ^3<0 , the salesperson cannot express only surprise throughout the presentation in the absence of other emotions; thus, this corner solution does not seem practically useful. In contrast, the face presence satisfies the conditions α^0=3.138>>0 and γ^0=−4.641<<0 , and the optimal F*=−3.138(−2×4.641)= .34. For comparison, the average face presence in Table 2 is.19. Thus, face presence should be increased from 19% to 34% to maximize sales. Moderation effectsModel 4 specifies the interactions of emotional displays with free shipping and price. Table 4 shows that the estimated ω^k are not significant for all k. Hence, the main effects of emotional displays hold regardless of the shipping fee waiver. Similarly, the price interaction effects of fear ( δ^4 ), anger ( δ^5 ), and disgust ( δ^6 ) are not significant, thereby generalizing their main effects across various prices.In contrast, the interaction effects of happiness ( δ^1 ), sadness ( δ^2 ), and surprise ( δ^6 ) are significant and negative. They moderate price elasticity: ηk=∂Ln(Q)∂Ln(P)=β1+δkzkP . Substituting δ^1=−.009 for happiness from Table 4 and the average price of $110.48 from Table 2, we get price elasticity η1=−.77−.99z1 , which means viewers become more price sensitive as the intensity of sellers' happiness increases. Why? Because the buyers suspect that the seller is gaining at their expense ([53]), and they exhibit ""move against"" tendencies (see Table 1). The qualitatively similar results hold for sadness and surprise. These interaction effects generalize our previous findings: emotional displays are bad for business. Marketing mix effectiveness in the presence of emotionsAccording to the log-linear specification, the estimated coefficient of.386 (see Model 2 in Table 3) means that, ceteris paribus, a product from category 1 sells 1.47 ( =Exp(.386)) times more than a product from category 2. The estimated price elasticity equals –.765 (see Model 2 in Table 3), which means a 10% increase in price corresponds to a 7.65% decrease in sales. Similarly, the estimated display duration elasticity equals.626 (see Model 2 in Table 3), which means a 10% increase in display duration corresponds to a 6.26% increase in sales, which is about 2 to 6 times larger than advertising elasticity (see [47]). The free shipping increases the quantity sold by.198% (see Model 2 in Table 3). Using the average price of $110.48 and the average quantity of 69.64 (see Table 2), the shipping waiver increases revenues by $15.23 (=$110.48×.198100×69.64) and is profitable when the shipping costs are below $16. Ranking salespeopleHuman biases affect the performance appraisal process (e.g., pay, bonus, advancement rate, prestige). We propose that to mitigate these biases, salespeople should be ranked on their individual effectiveness (objective attribution) rather than average sales (naïve attribution). Model 1 facilitates the estimation of the effectiveness of an individual salesperson by controlling for prices, duration, free shipping, time of day, and week. Table 3 reports the estimates of percentage sales increase for an individual salesperson relative to the group average based on effects coding of the dummy variables (see [22]). Consider the estimate of –.488 for salesperson 1 from Model 1 in Table 3. That estimate means salesperson 1's performance is.488% below the group average. Similarly, salesperson 6's performance is.083% above the group average. These estimated effects are not affected by human cognitive biases.Graph: Figure 5. Salesperson performance appraisal.We compare the salesperson's performance rank based on the naïve versus objective attributions. Panel A in Figure 5 shows the ranking of 23 salespeople based on the average sales, which ignores the effects of prices, duration, free shipping, and time of day and week. In contrast, Panel B shows the ranking of the same 23 salespeople based on their individual effectiveness. The top and the bottom three salespeople remain the same under both metrics, thereby showing that the ranking attains convergent validity by identifying the same set of best and worst performers. However, the majority of salespeople (∼75%) reside in the middle, where the rank ordering differs across metrics. Thus, the objective attribution based on salesperson's effectiveness after controlling for prices, duration, free shipping, and time of day and week should guide supervisors in more objectively selecting salespeople for rewards, recognition, and retraining. Understanding moderation effectsFigures 6 and 7 depict the sales impact of emotional displays based on the full model (Model 4). In these figures, a low (high) price refers to the 25th (75th) percentile of the price distribution. First, emotional displays decrease sales. This finding holds uniformly for negative and positive emotions. Because Ln(Q) serves as the dependent variable, the marginal change in it equals ΔQ/Q , which represents ""percentage change in sales."" Thus, a marginal increase in emotional display corresponds to a sales decline that ranges from.004% to.18%. Across the six emotions, the magnitude of the total sales decline (.47%) is more than double the free-shipping effect (.20%). Second, because the tangent to the curves in Figures 6 and 7 becomes steeper as the intensity of emotional display increases, the sales decline accelerates. In other words, the sales decline increases at an increasing rate. Thus, not displaying emotions emerges as the optimal course of action. So, salespeople should sell with a neutral face, although how consumers interpret ""neutral"" depends on the sellers' gender, facial morphology, and contextual factors (e.g., [23]). Finally, the parallel curves in Figures 6 and 7 reveal the modest magnitude of moderation effects: sales decrease as price increases or promotion decreases (see the dashed curves).Graph: Figure 6. Emotional displays by price interactions.Graph: Figure 7. Emotional display by free shipping interactions. Time effectsIn the week effect, sine and cosine variables jointly identify the sales variations across days of the week. The cosine variable differentiates the first half of the week (Monday to noon Thursday) from the second half of the week (noon Thursday to Sunday). The sine variable differentiates the middle of the week (9 p.m. Tuesday to 9 p.m. Friday) from the end of the week (9 p.m. Friday to 9 a.m. Tuesday). Similarly, in the day effect, the sine and cosine variables identify the sales variations across hours of the day. Specifically, the cosine variable differentiates post meridiem (p.m.) from ante meridiem (a.m.), while the sine variable captures the rhythms across the late evening (6 p.m. to midnight) through the night hours (midnight to 6 a.m.) to the morning hours (6 a.m. to noon) and the afternoon (noon to 6 p.m.). Because the empirical results indicate that the cosine variable is less important than the sine variable, the a.m./p.m. distinction is not critical. As expected, sales occur 24 hours a day, including the nights; peak during the day; and are larger during the weekends. Relative variable importanceFigure 8 presents the relative contribution of marketing mix and nonmarketing variables: the former contributes 71%, whereas the latter accounts for 29% of the total R2=80% . The time of day, the day of the week, and the week of the year explain 20%. Emotional displays and face presence further explain 9% of the explained variance. Thus, nonmarketing variables boost explanatory power.Graph: Figure 8. Variable importance. Robustness checks Parameter stabilityA glance across the columns in Table 3 indicates a remarkable robustness. The columns reveal the estimated marketing mix effects in the presence of various operationalizations of emotional displays. For example, across Models 1–4 the price elasticity varies from –.76 to –.77, and the duration elasticity ranges from.55 to.67. Likewise, shipping and product estimates are (.22,.41), (.20,.39), (.19,.36), and (.19,.36) across Models 1–4, respectively. Salesforce effectiveness across the four models is also stable; for example, the percentage sales increase due to salesperson 15 hovers around.42. Even the rhythms of daily and weekly sales deviate only marginally. These results hold even when we replaced the static face variable Fis in Equation 3 with the dynamic component z0,is=∑t=130e^0tFis,t across the epochs t=1,⋯,30 . Furthermore, we tested for heterogeneous effects of emotional displays and found that the effects were homogeneous across the two product categories (see Figures 6 and 7). Thus, the broad robustness —for all the variables and across all the models— enhances confidence in these results. Tercile analysisWe also analyzed data by splitting the presentation span of an item i displayed in a show s into three time segments. We discovered V-shaped effects across the beginning, middle, and end of the presentation span for all the six emotions, including happiness. We then extended this analysis tenfold to 30 epochs and found that not only does the parameter stability hold in both analyses, but also qualitatively similar results persist: negative U-shaped effects of emotions over the presentation span. Furthermore, the average variance inflation factor across all independent variables was 1.66, ranging from 1.01 to 5.17, which is far below 10 and thus rules out multicollinearity concerns. Dynamic time warpingTo our knowledge, this study marks the first time dynamic time warping appears in marketing. To further assess robustness, we reestimated Model 2 without dynamic time warping. As mentioned previously, dynamic time warping aligns landmarks such as the peaks, valleys, and inflections of the raw emotional curves Eisk(t) . Such landmark alignment homogenizes the timing of peaks, valleys, and inflections in Eisk(t) . Consequently, the estimated trajectories, a^kt , in Figure 3 are smoother than those in Figure 4 without landmark alignments. More importantly, the overall pattern remains the same: the sales impact a^kt is negative, U-shaped, and similar across the six emotions. In summary, the U-shaped patterns as well as other results are robust. We close this section by comparing the performance of models on multiple metrics. Model comparisonWhich one of the four models is the best? Although the adjusted R2 of about 80% is remarkable, especially given 99,451 sales pitches, it does not discriminate among the four models as the log-likelihood, Akaike information criterion, and Bayesian information criterion do. Therefore, we compared the models using these metrics and present the results in Table 5. Specifically, Models 3 and 4 dominate Models 1 and 2 on all the metrics. The Bayesian information criterion selects Model 3, whereas both the other metrics (log-likelihood and Akaike information criterion) indicate that Model 4 outperforms the rest. We used Model 4 to plot Figures 6 and 7. We next discuss the implications of these findings.GraphTable 5. Models Comparison. DiscussionThis research offers important insights into livestream retailing by addressing two foundational questions identified in this special issue dedicated to understanding the interface of technology and marketing: ( 1) How can managers use new types of data to improve marketing decision making? and ( 2) What new methods can deliver the best consumer insights to improve marketing strategy? To address the first question, the second pennant in Figure 1 captures the new type of data available from streaming videos of sales presentations, which can identify a large-scale, unobtrusive, and comprehensive set of emotions. To address the second question, the fourth pennant in Figure 1 contributes the new methods to create six emotional trajectories via functional principal components analysis and dynamic time warping to align them. Incorporating them as quadratic and moderating variables, we then assess the value of emotional displays. Building on Models 1–4, we discuss the following theoretical and managerial contributions. Theoretical Contributions Livestreaming technology opportunities in marketingLivestream e-commerce, which features hosts promoting and selling goods and services in real time via screen-mediated sales presentations, represents an emerging opportunity for marketers to create, deliver, and communicate content so as to monetize in ways not possible previously. Specifically, marketers can, first, reach customers via new channels such as social messaging apps (Facebook, WeChat), livestreaming services (e.g., Twitch), and internet platforms (e.g., Taobao Live) that integrate shopping and entertainment. Second, these technology platforms facilitate purchases from wherever and whenever customers are seeking to buy. Third, they shorten the purchase funnel by demonstrating a product and describing why it is a must-have item; conveying that only limited quantities are available; counting down the time remaining on the item before the next item is to be introduced; and injecting such calls to action as ""grab it before its gone."" Finally, technology allows value capture in formats not possible previously: noncash payments (e.g., PayPal, Venmo), installment payments (e.g., Klarna unsecured loans), and barter payments (e.g., BarterOnly.com, which provides exchanges of used products). Marketers need to imagine how they can integrate such value creation and value capture opportunities made possible by technological advances. Large-scale unobtrusive emotions dataEarlier studies used human intervention to collect data on emotional displays at a small scale (e.g., [ 8]; [30]; [42]). In contrast, applying artificial intelligence (see, e.g., [33]; [35]), we extract face presence and facial expressions from 62.32 million frames of streaming video sales presentations automatically and unobtrusively, thereby responding to calls to harness machine learning to generate meaning from big data (e.g., [ 4]; [29]; [40]; [59]). Multiple emotionsEarlier studies consider either a single (e.g., [32]; [57]; [58]) or a select few emotions (e.g., [10]; [50]; [54]; [55]), potentially resulting in biased estimates due to omitted variables. Hence, Model 2 specifies a comprehensive set of six emotional displays simultaneously. Our focus on salespeople's emotional displays is also responsive to an earlier call to devote greater attention to emotions on the seller side of the exchange dyad (see [ 3]). Dynamic effectsEarlier studies focus on static episodic expressions (e.g., [10]; [55]). Our Model 2 permits capturing the dynamic trajectories of emotions at a more granular level, thereby revealing time-varying patterns of sales impact (see Figure 3). More importantly, the Web Appendix makes original contributions to the theory of inference on the effects of functional principal components. Optimal emotionsVirtually all studies on emotions have used customer mindset metrics as the dependent variables (e.g., [54]; [57]; [58]). While [32] use box-office revenues, they specify happiness to monotonically affect sales, ruling out the possibility of that an optimal level of emotions exists. Given the nonmonotonic effects in Models 3 and 4, the theoretical existence of the optimal mix arises. Equation 4 presents the optimal emotions to display so as to maximize sales. These results not only make original contributions to the extant literature but also offer guidance to design technology-inspired service agents (e.g., avatars, virtual news anchors) to be more humanlike ([11]; [37]). They also inform the discussion about technology and marketing in that artificial intelligence can be used to monitor the seller's facial activity, provide real-time coaching, and thus assist in training salespeople to improve business outcomes ([20]; [33]). Salesperson's impact in screen-mediated exchangesA marketplace increasingly characterized by greater technological connectivity and interactivity has prompted calls to investigate the business impact of a seller's facial expressions in screen-mediated commercial interactions ([ 7]). [28], for instance, underscore the need to evaluate whether their results from in-person, face-to-face customer encounters involving ""emotionally calibrated"" salespeople will hold in digital exchanges. The authors contend that this type of salesperson exhibits calmness and that exuding calmness builds rapport, which in turn drives favorable sales performance outcomes. Our theorizing, which is steeped in EASI's predictions about the inferences that buyers draw about a seller's facial expressions in a competitive exchange (see Table 1), and findings from a one-to-many livestream broadcast setting reaffirm the importance of reducing emotional displays in driving sales effectiveness. We thereby contribute to understanding the communicative role of facial expressions in screen-mediated exchanges and elaborate further in the following section on ""selling with a straight face."" Managerial Contributions Optimal face allocationHow should face presence be allocated over an item's presentation span? Should frames containing a face be displayed uniformly or in chunks? If the latter, should they be concentrated in the middle, when sales resistance is highest? To this end, we evaluate Equation 4 and present the optimal percentage allocation of the total number of frames with a face over an item's presentation span in Figure 9. We observe that the optimal allocation is neither uniformly displayed nor chunked in the middle. Rather, the optimal number of face frames wanes at the start, gradually builds to a crescendo, and eventually ebbs. Specifically, the optimal allocation decreases on the initial 10% span, then gradually increases as the presentation progresses, and finally decreases in the last 15% span. Remarkably, this optimal allocation conforms to the three-part structure of stories: the beginning, the middle, and the end (see [36]). Sell with a straight face?Figures 6 and 7 uncover the novel and provocative findings that, first, positive emotional displays reduce sales. Second, the greater the intensity, the larger the decline. To mitigate the negative effect, salespeople should consider toning down their facial expressions. To mitigate the quadratic effects of intensity, they can abate their exaggerated expressions. Together, these findings indicate a new maxim: sell with a straight face. Consistent with this maxim, [12], p. 82) advocates that direct marketers should use a ""journalist approach"" to answer the ""who, what, why, when, where of a product. Whom is the product for? What does it do? Why is it beneficial? When can it be used? Where can it be bought?"" In other words, livestream salespeople should broadcast their pitch with a stoic expression akin to that of news anchors, though we acknowledge that this implication may not generalize to face-to-face communications in business markets. Sales resistance curveFigures 3 and 4 can be interpreted as the sales resistance curve. The maximum sales resistance is near the middle of an item's presentation; the least sales resistance is at the beginning and end of presentations. This U-shaped sales resistance curve provides actionable guidelines to practitioners. Emotional displays at the beginning and end of presentations help engage consumers and build rapport. However, during the livestream show, hosts should monitor the frequency and intensity of their emotional expressions. Because genuine interactions involve less emotional and more neutral expressions, salespeople can make emotional connections with the audience with neutral expressions and lessen the insidious effects of sales resistance. Although hosts cannot completely avoid emotions, they should take advantage of livestreaming platforms to emotionally connect the brands with customers. People analytics in sales performance appraisalA naïve assessment of a salesperson's performance is based on the actual quantity sold. However, this quantity depends on factors such as prices, duration, free shipping, and time of day and week. In other words, a salesperson can validly object that another salesperson's larger actual sales are not reflective of his/her performance alone because price, duration, free shipping, and time of day and week also impacted sales. Cognitive biases further compound such performance appraisals. Prominent factors driving cognitive biases include the fundamental attribution error, halo effects, the leniency bias, the recency bias, selective perception, the self-serving bias, and the similarity bias (e.g., [48]). Fundamental attribution error refers to supervisors underestimating the influence of external factors and overestimating the influence of internal factors when judging a salesperson's performance; halo effects arise when a general impression of a salesperson overshadows the relevant metrics; leniency bias refers to a supervisor's tendency to rate all salespeople positively (or negatively), reducing the difference between top and bottom performers; recency bias creeps in when recent events (e.g., bumper sales, sharp declines) influence supervisors' judgments; selective perception refers to the supervisor's tendency to notice certain metrics and filter out others; self-serving bias emerges when a salesperson attributes own successes to internal factors and failures to external factors; and similarity bias (i.e., homophily) shapes the evaluation when supervisors reward a salesperson similar to themselves.Using Table 3, managers can objectively rank salespeople (see Panel C in Figure 5) to circumvent the effects of the aforementioned biases. Indeed, the CEO and senior leadership team of the livestream retailer we examined found our proposed framework valuable to recognize excellence and identify candidates for retraining. Both recognition and training, in turn, help improve future sales performance ([61]). Thus, the framework in Figure 1 unlocks the power of data and contributes to sales performance analytics. Future Research Content effects[26]) recent study suggests that information content can matter. Specifically, they analyze 275 sales pitches from the Home Shopping Network, manually code the minute-by-minute content on the cumulative sales thus far during an item's presentation span, and show that the intermittent availability of this information increases item sales by.084 units at the onset and decreases linearly to.015 units at the end for a unit increase in the displayed cumulative sales. We encourage future researchers to automate such content analysis to extract and incorporate facial expressions. Two-way communicationsOur empirical study pertains to one-to-many screen-mediated competitive exchanges, and it shows that the salesperson's emotional expressions evoke negative inferences by viewers about the salesperson's intentions. One explanation may be the absence of social interaction. When the salesperson smiles, the viewers may not reciprocate because the salesperson's emotions are not targeted to a specific viewer. Such differences provide the impetus to study screen-mediated face-to-face interactions in the presence of social others. For example, [21] contend that the customer purchase journey involves traveling with social others, which necessitates investigations into the various influences that members of the social network can have on buyers' appraisals, intentions, and actions. In a livestream e-commerce setting, the host becomes an important social other. Viewers can readily communicate with the influencer via live chat texts, emojis, voice, and/or video and further enhance their sense of connection with that celebrity (i.e., parasocial relationship). Does the host's verbal and nonverbal communications influence viewers' behavior in such communal, two-way screen-mediated exchanges? Do purchases by social others induce ""fear of missing out""? [43] suggests conversion rates of 30% in livestream shopping versus 3% in traditional marketing. To incorporate such two-way communications in the models, researchers should augment the regressors with the characteristics of not only the items and sellers (as in this study), but also the network of social others and the hosts. We encourage further research to shed light on two-way communications in livestream shopping. Authentic emotions[25] manipulate authenticity (i.e., surface or deep acting) and emotional intensity in simulated service encounters (i.e., actors played the role of employees) with 223 consumers to understand the effects on customer satisfaction, customer–employee rapport, and loyalty intentions. They show that authenticity rather than intensity influences customers' reactions (for similar results, see [57]). We encourage future researchers to design emotion recognition algorithms that can classify facial expressions on the basis of emotional authenticity in addition to intensity. ConclusionPrevious studies predominantly focus on marketing mix effects on sales because when they were conducted, machine learning technology was not available to detect faces and extract emotions at scale. This study combines machine learning technology and marketing. Specifically, we develop the retail analytics engine (see Figure 1) to unobtrusively collect data on face presence and emotional displays. Applying this technology to livestream retail data, we found that facial expressions, including happiness, adversely impact sales. This counterintuitive and provocative finding suggests that salespeople should sell with a straight face. These negative effects exhibit U-shaped dynamics over an item's presentation span, uniformly across six emotions, revealing that the largest sales resistance occurs during the middle of the presentation. Furthermore, the presence of a face matters because it impacts sales positively; therefore, it should be present more than is currently the case. Yet, its optimal allocation over time should be reduced over the initial 10% span, then gradually increased as the presentation progresses, and subsequently tapered down in the last 15% span. Finally, the retail analytics engine empowers managers to more objectively assess the effectiveness of each individual salesperson (see Figure 5), thereby circumventing cognitive biases in performance appraisals.This study highlights the importance of monitoring and managing facial expressions. One implication is to train new salespeople. The firm can analyze the video footage, much like sports teams watch films of critical moments in previous games to learn what individual players did well and not so well, and sales coaches can help discern the extent to which they displayed emotions and the proportion of each emotion expressed. The feedback from such debriefing sessions could be used to modify sales pitches. Another implication is to retrain experienced sales professionals. The firm can compare each salesperson with the top performer (see Figure 5) and identify which emotions the salesperson ought to tackle. Happiness is the first one that should be addressed. While previous research advocates ""service with a smile,"" we suggest selling with a straight face. Smiling may be off-putting because it lacks authenticity ([25]), reducing trust in the seller ([10]). Subsequently, salespeople should address displays of anger, then fear, and other negative emotions. Last, this study has implications for bot marketing. As technology advances, bots will more closely mimic human facial expressions and supersede humans in monitoring and managing facial expressions. Chat bots, like humans, provide voice assistance to customers. Similarly, three-dimensional audiovisual bots, like salespeople, can engage with customers. For example, HSBC Bank in Northern California employs Pepper, a social humanoid robot ([14]). Further technological advances will bestow bots with the ability to express and reciprocate emotions, thereby assisting livestream retailers to nudge prospective customers through the purchase funnel by explaining features and benefits, instilling urgency to buy, and entertaining them along the way. " 2,Analyzing the Cultural Contradictions of Authenticity: Theoretical and Managerial Insights from the Market Logic of Conscious Capitalism," This research analyzes the cultural contradictions of authenticity as they pertain to the actions of consumers and marketers. The authors' conceptualization diverges from the conventional assumption that the ambiguity manifest in the concept of authenticity can be resolved by identifying an essential set of defining attributes or by conceptualizing it as a continuum. Using a semiotic approach, the authors identify a general system of structural relationships and ambiguous classifications that organize the meanings through which authenticity is understood and contested in a given market context. They demonstrate the contextually adaptable nature of this framework by analyzing the authenticity contradictions generated by the cultural tensions between ""conscious capitalism""—a market logic that encompasses both global brands and small independent businesses, such as a farm-to-table restaurant or an organic food co-op—and the elitist critique. The Slow Food movement provides a case study for analyzing how consumers, producers, and entrepreneurs who identify with conscious capitalist ideals understand these disauthenticating, elitist associations and the strategies they use to counter them. The authors conclude by discussing implications of the analysis for theories of authenticity and for managing the authenticity challenges facing conscious capitalist brands.","Consumers crave authenticity—so much so that their quest for authenticity is considered ""one of the cornerstones of contemporary marketing"" ([11], p. 21). This has created an enormous challenge for the field, considering that marketing itself is typically considered inherently inauthentic. —[67], p. 1)In the field of marketing, little doubt exits that ""authenticity"" is highly desired by consumers and thereby is a crucially important strategic resource for marketing management. Consumers are more likely to form stronger emotional attachments to a brand, business, or tourist site they perceive as being authentic ([20]; [30]; [57]; [86]) and to incorporate these market resources into their identities ([ 7]; [11]; [45]). On the managerial side, [31], p. 610) conclude that authenticity is ""the most rare and coveted asset in the contemporary branding landscape."" Their assertion is supported by an array of studies indicating that authenticity is integral to the enhancement of brand equity ([60]), effective brand extensions ([82]), persuasive marketing communications ([ 5]), success in relationship marketing ([22]), and emotionally engaging person and celebrity brands ([31]; [87]).Although there is a clear consensus that authenticity profoundly matters to both consumers and marketers, the marketing literature also presents a recurrent concern that authenticity is a nebulous concept that has eluded precise definition ([ 5]). [67], p. 2) proclaim that this conceptual ambiguity poses a significant barrier to creating ""a coherent theory of authenticity."" Accordingly, they aim to redress this dilemma by presenting a general definition of authenticity based on six key perceptual components. In contrast, [81], p. 3) proposes that ""authenticity is a polysemous and multilayered concept"" and thus ""it might not [emphasis added] be helpful to compress the wealth of disparate meanings associated with the concept into a single definition.""As Södergren further notes in his meta-analysis, ""the majority of the research [on authenticity] has focused on characteristics that distinguish the 'real thing' from the fake"" (p. 11). To further elaborate on this conceptual tendency, marketers' efforts to define authenticity almost invariably invoke some variant of genuineness, such as brands (via their management teams) staying true to ideals of timeless tradition, heritage, craftsmanship, and quality (see also [ 6]; [82]). In this spirit, [55], p. 30) propose that the authenticity of a brand's social media communications hinges on perceptions of honesty, sincerity, and being ""real."" [ 7] similarly contend that the core cultural meanings of authenticity are truth, genuineness, and reality. [67] comprehensive definition of authenticity also incorporates a series of veracity-oriented constructs, such as originality (i.e., not being a copy), accuracy (i.e., being true to others), and integrity (i.e., being true to oneself).While the analytic goal of distinguishing the authentic from the inauthentic makes intuitive sense, it is a Sisyphean undertaking that attempts to specify an ambiguous cultural category by referring to other semantic terms whose meanings are also contextually contingent and malleable (i.e., honesty, sincerity, originality, genuineness, and truthfulness). Furthermore, informing marketing managers that their brand lacks authenticity because consumers see it as being unoriginal, insincere, or dishonest offers little guidance on how to resolve the deeper cultural tensions that drive these unfavorable perceptions. Rather than a checklist of definitional attributes, we argue that marketing managers need an analytic approach that can enable them to answer questions such as ( 1) why is their brand or business susceptible to certain kinds of authenticity challenges?, ( 2) what cultural meanings and contradictions underlie those challenges?, and ( 3) what responses could they take to mitigate the disauthenticating associations that ensue from these tensions?Returning to our opening vignette, we can reframe [67], p. 1) statement that marketers face an ""enormous challenge"" because their profession is ""typically seen as inauthentic"" (see also [ 5]) as a realization that marketing, as a business practice, also occupies an ambiguous cultural position. On the one hand, marketing aims to advocate for the needs (and voices) of customers ([41]) and, yet, it is also means for companies to enhance their profits and market share. This tension readily gives rise to concerns that short-term (and potentially exploitive) profitability goals might take priority over serving customers' best interests. Accordingly, consumers are inundated with cultural narratives (ranging from journalistic reports about deceptive marketing tactics to portrayals of unscrupulous marketers by entertainment media) that encourage cynicism and distrust toward marketers' branding claims and persuasive communications ([32]; [45]; [66]).However, the specific cultural meanings and associations that lead to perceptions of authenticity or inauthenticity vary across brands and markets. For example, consumers are likely to deploy different configurations of meanings, beliefs, and evaluative norms when judging the authenticity of a high-fashion retailer ([23]), a café owner who promotes their establishment as a home-away-from-home ([20]) or a global brand that positions itself as an advocate for environmental justice (e.g., Patagonia; [49]).In this article, we explain and demonstrate how the semiotic square ([40]) can be used to systematically analyze such culturally heterogeneous authenticity contradictions and to develop contextually appropriate responses to the specific authenticity challenges that arise in a given market. The semiotic square is an analytic tool that has often been used to delineate cultural meanings and semantic contradictions that are manifest in both consumer perceptions and marketing strategies ([29]; [36]; [50]; [51]; [54]; [68]; [69]). From a semiotic perspective, the cultural categories of the authentic and the inauthentic are not just contrasting or oppositional terms. Rather, they are anchor points in a broader network of relationships through which the authenticity of a given brand, business, brand ambassador, social media influencer, and the like is culturally constructed and potentially contested.Our market context is conscious capitalism, which refers to a ""way of thinking about capitalism and business that better reflects where we are in the human journey, the state of our world today, and the innate potential of business to make a positive impact on the world"" ([62], p. 273). Conscious capitalism is particularly vulnerable to the broader authenticity–inauthenticity tension that all marketers confront to varying degrees. Therefore, it serves as a very relevant and informative context for our analysis.Conscious capitalism's key premise is that capitalism's societal purpose has, historically, been defined too narrowly (i.e., maximizing shareholder wealth and optimizing consumers' market choices) and, accordingly, its society-enhancing potential remains greatly underutilized. Rather than grafting a social mission onto a traditional profit-maximization model, as per conventional corporate social responsibility approaches, proponents of conscious capitalism contend that businesses should place value-driven goals and social consciousness at the core of their institutional missions ([62]).By aiming to redefine the nature and function of capitalism, conscious capitalism can be analyzed as a market logic that transcends its iconic brands (e.g., Patagonia, Starbucks, TOMS, Whole Foods) or socially conscious businesses (e.g., a cooperatively owned, fair trade, local coffee shop). As discussed by [27], pp. 40–42), a market logic is an integrated network of meanings, values, and norms that provide ( 1) principles that can guide thoughts, actions, and preferences; ( 2) vocabularies of motivation and justification; and ( 3) material and symbolic resources for constructing an identity (such as being an ethical consumer or a purpose-driven business owner).Conscious capitalism organizes a constellation of ideologically aligned brands and an even larger network of businesses that have different scales of operation and serve different roles in the supply chain. Thus, consumers who support this array of brands and enterprises have access to a set of normative principles to guide their purchase choices (e.g., locally sourced materials are preferred over imported ones, plastic product packaging should be avoided); they learn an intricate system of terms and codes (e.g., ""postconsumer recycled content,"" third-party certification labels such as the Rainforest Alliance or Certified Carbon Neutral); and they can express their socially conscious sensibilities through an array of consumption practices—wearing a Patagonia fleece, driving an electric car, shopping at a farmers' market, brandishing a reusable Whole Foods' canvas tote bag, buying fair trade chocolate, or supporting a farm-to-table restaurant.In the general public discourse, however, the authenticity of conscious capitalist brands and businesses, and their consumer supporters, is frequently called into question. These authenticity challenges are sufficiently problematic that leading proponents of conscious capitalism feel compelled to address them:There is a growing network of people building their companies based on the idea that business is about more than making a profit. It's about higher purpose ... and the innate potential of business to make a positive impact on the world.... But one of the most predictable responses we get from people when we mention the idea of conscious capitalism is, ""That's an oxymoron!"" ([61])Conscious capitalism's authenticity challenges hinge on a cultural tension between the profit-maximizing ethos of capitalism and the ennobling idea that capitalist enterprises can serve higher societal and moral purposes that supersede commercial interests ([ 3]; [33]). In this vein, critics often suggest that conscious capitalism deploys the language of sustainability and other socially beneficial goals for the instrumental purpose of catering to higher-income consumers who will pay a premium to imbue their consumption practices with an aura of moral virtue:Thus, the rise of social enterprises [i.e., conscious capitalist enterprises] has been met with hostility, particularly toward its authenticity and its sustainable impact. If their goods and services continue to be priced as they are, is the sustainable movement only for the demographic that can afford it? ([14])[70], p. 73) similarly argue that conscious capitalism is more promotional hyperbole than a viable business reality and, further, add this reservation: ""It is important to note that the firms associated with the Conscious Capitalism movement are far from a random sample of American businesses: In fact, a great many sell relatively expensive products to relatively affluent, socially- or health-conscious consumers.""This incredulous and, at times, adversarial public response to conscious capitalism has not arisen ex nihilo. Rather, it draws from a cultural narrative that we characterize as the ""elitist critique."" As historian [34] elaborates, the political charge of elitism has evolved from its classic populist roots, which railed against the undue power wielded by the captains of industry and affluent political insiders, to an antipathy toward the intellectual class (who may not be unduly wealthy or politically powerful). Through this shift, the charge of elitism was distanced from its origins in economic conflicts between the working class and the owners of capital (and their management intermediaries) and became repositioned in a culture war rift whereby ""the 'elite' could be identified by its liberal ideas, coastal real estate, and highbrow consumer preferences"" ([34], emphasis added).We investigate how the specific authenticity challenges posed by the elitist critique of conscious capitalism are negotiated by consumers and producers in the context of the Slow Food movement ([89]). Slow Food encompasses an array of ideologically aligned brands, enterprises (farm-to-table restaurants, artisan producers, and organic and free-range farmers), consumption practices (e.g., shopping at a farmers' market or a local co-op), and goods and services (e.g., an heirloom tomato, grass-fed beef, a class in fermentation techniques). Slow Food's signature issues and social change goals are grounded in the market logic of conscious capitalism, including local sourcing, fair wages for workers, sustainable modes of production, environmental awareness and habitat protection, and a broader project of redressing societal ills through the coordinated actions of socially conscious businesses and consumers (see [73], [74]). The elitist critique has also become part and parcel of Slow Food's brand image, and it poses salient authenticity challenges for Slow Food's producers, entrepreneurs, and consumers.In the following sections, we first discuss the key analytic premises of the semiotic square. Next, we develop a semiotic conceptualization of authenticity that maps out its structural contradictions (and ambiguous classifications). We use this analytic framework to explicate the ways in which the elitist critique gives a particular cultural form to the authenticity contradictions plaguing the market logic of conscious capitalism. We then profile the authenticating strategies that Slow Food advocates (consumers, producers, and restauranteurs) use to counter these disauthenticating elitist associations. We conclude by discussing the implications of this analysis for theories of authenticity and for managing the authenticity challenges facing conscious capitalist enterprises. The Semiotics of Authenticity The Semiotic Square as an Analytic ToolFrom a semiotic perspective ([40]), the meaning and categorical boundaries of a given concept are defined through relations to what it is not. For example, the cultural meanings of masculinity have been historically established through contrasts to those that have defined femininity and the related nexus of ever-changing ideals, values, and practices through which this binary contrast has been culturally articulated and transformed over time ([50]). These structural relations give rise to ambiguous categories whose associated cultural meanings can become points of contestation and debate, such as in the cases of ""metrosexuals"" ([78]), stay-at-home dads ([19]), or the ongoing controversies sparked by the category of transgender athletes ([12]).The binary opposition between authenticity and inauthenticity presents a similar arrangement of contradictions and ambiguous classifications. Consequently, we propose that authenticity is not a set of discrete properties that distinguish the genuine from the fake—but, rather, an ongoing process of managing a network of contingent relationships. In some markets, for some brands and enterprises, these contingencies may be more stable, whereas in others, they may become more culturally contested and, thus, unstable. We suggest that conscious capitalist brands and businesses, owing to the elitist critique, exemplify this latter and more managerially challenging case.Figure 1 presents a semiotic square representation of authenticity. In this article, we use the ""contradictions of authenticity"" as an integrative term that encompasses the structural relations among the semiotic 3Cs (contrariety, complementarity, and contradictory relations).Graph: Figure 1. A semiotic model of the authenticity–inauthenticity opposition.The horizontal arrows represent contrariety relations. These relations are roughly analogous to the standard binary oppositions that anchor semantic differential scales. However, relations of contrariety further indicate that the meaning of a term is defined through a relationship to its binary contrast (e.g., good is understood relative to evil). Accordingly, the meaning of authenticity is always contingent on the operative meaning of inauthenticity, and vice versa. We refer to the authentic ↔ inauthentic contrariety as the primary contrariety relation because it represents the dominant tension that, in turn, sets the complementary terms for the secondary contrariety relation (i.e., not inauthentic ↔ not authentic).The vertical arrows represent complementarity relations. Such conceptual pairings are compatible and noncontradictory (but are not necessarily synonymous or interchangeable). For example, ""not inauthentic"" is congruent with the dominant term, authentic. However, this classification also harbors other connotations and, thus, ambiguous meanings. For example, imagine a painting created by a famous artist, say Picasso, who at the time was a fledgling beginner, imitating the style of another painter. Because the painting does not evince Picasso's quintessential artistic motifs, its authenticity becomes ambiguous (and debatable)—that is, at what point in his career does a painting by Picasso truly become a ""Picasso""? The term ""not inauthentic"" conveys this type of ambiguity.The diagonal arrows represent contradictory relations. These relations indicate that any entity or action deemed to be authentic (or inauthentic) will harbor some qualities that can be judged as contradicting such an assessment. As an illustration, let us again consider the idea of artistic authenticity. From a conventional standpoint, the authenticity of an artist, even a renowned one, can always be challenged on the grounds that their creations exhibit properties that are derivative of other genres, styles, or artistic predecessors (authentic ↔ not authentic). Conversely, the art world's postmodern movement disavows the idea of artistic originality and, instead, celebrates that all artistic productions are, in some sense, a reworking of something prior. As exemplified by Andy Warhol's replications of iconic cultural images (Coca-Cola bottles, Campbell Soup cans, the face of Marilyn Monroe), postmodern art is also heralded for its capacity to surprise and inspire revelatory aesthetic experiences through its creative (and often ironic) uses of repetition, collage, assemblage, montage, and bricolage (inauthentic ↔ not inauthentic) ([43]).[ 4] A Semiotic Conceptualization of AuthenticityIn Figure 1, the cloud-like drawings represent the specific cultural meanings that give contextual form to the contradictions of authenticity. For purposes of our analysis, the relevant meaning systems are the market logic of conscious capitalism and the elitist critique. This system of semiotic relationships gives rise to four emergent (and ambiguous) classifications, each harboring latent contradictions. In discussing these ambiguous categories, we first illustrate them in more general terms and then address their manifestations in the context of conscious capitalism and the elitist critique. Authentic + not inauthenticThis complementarity relation corresponds to what [39] discuss as indexical authenticity. In this usage, an index refers to a given object or behavior—for example, the actions of a whitewater raft guide, handprints in front of Grauman's Chinese Theater, a painting, or a branded good. Indexes are classified as authentic when they are believed to possess a factual and spatiotemporal connection to some validating condition. For example, consumers will judge the actions of a whitewater raft guide as authentic if they are believed to reflect an inner passion for the outdoors (rather than being a calculated performance done for remunerative purposes; [ 2]). Similarly, consumers will typically deem a branded good to be authentic when they believe its design, production, and quality certification has proceeded under the auspices of those who own or manage the brand of note.As these examples suggest, perceptions of indexical authenticity can be more or less certain. As an example of higher certainty, Prada certifies the genuineness of its handbags by assigning each a unique and traceable serial number that is documented on an authenticity card. On the less certain side, customers have to infer the indexical authenticity of a whitewater raft guide's passion for the outdoors or a retail associate's expressions of friendliness and interpersonal concern. In these cases, consumers' judgements about the authenticity (or inauthenticity) of a marketer's actions (or the actions of other consumers) depend on their inferences about underlying motivations and intent.The elitist critique provides a constellation of culturally shared meanings and rationales that support disconfirming suppositions about the indexical authenticity of conscious capitalist enterprises and their consumer followers. These disauthenticating associations directly correspond to the ambiguous categories emerging from the primary contrariety (authentic ↔ inauthentic), the secondary contrariety (not inauthentic ↔ not authentic), and the complementarity relation of inauthentic ↔ not authentic relations. Authentic + inauthenticThis ambiguous classification corresponds to seemingly oxymoronic constructions such as authentic reproductions—or, in semiotic vernacular, ""iconic authenticity"" ([39]). In this usage, the icon is an object that is a known facsimile of an original referent and that is appreciated for its mimetic properties, as in the case of a comedian doing an uncanny impression of a celebrity. For the category of iconic authenticity, the ensuing goal is to present a compelling sense of verisimilitude through a meticulous recreation of the original referents' characteristics. Iconic authenticity is pursued by, among others, members of the cosplay community ([80]) and consumers who perform in historical recreations, such as Civil War reenactments ([17]). In a different market application, iconic authenticity would also be highly relevant to a budget-conscious consumer who wants to buy a convincing counterfeit version of an expensive designer brand.When situated in the context of the elitist critique, the ""authentic + inauthentic"" category assumes less favorable meanings of moral pretentiousness and hypocrisy. In this disauthenticating cultural frame, affluent (and typically left-leaning) consumers use conscious capitalist brands and goods to distinguish themselves from the price-conscious mainstream and their socioeconomic peers who display affluence through more ostentatious lifestyle choices ([24]). By claiming the mantle of moral virtue, such consumers can pursue social distinction in an otherwise orthodox manner—that is, through material displays of refined tastes ([ 9]; [44])—while appearing to disavow materialism and status consciousness.For example, during its heyday as a cultural icon, the Toyota Prius inspired oppositional brand communities (Muñiz and O'Guinn 2001) who referred to the vehicle (and its drivers) as ""the pious."" This epithet suggested that Prius drivers evinced a self-aggrandizing ""holier than thou"" stance that amplified the moral merits of their automotive preferences relative to those who made different choices ([59]). In a similar cultural vein, [13] note that ecofriendly consumers who purchase organic foods, drive electric luxury cars, and use natural cleaning products typically lead lifestyles that carry a much higher carbon footprint than lower-income consumers who live in smaller housing units, rely on public transport, and seldom fly. Seen in this critical light, such ecoconscious (affluent) consumers are virtue signaling ([24]; [42]; [90]), but their pretense of moral superiority is not warranted by these symbolic acts. Not inauthentic + not authenticThis ambiguous classification highlights that perceptions of genuineness (often taken as the sine qua non of authenticity) are a necessary but not insufficient condition for ascribing this honorific appellation to an object or action. That is, an entity or action may be deemed as genuine (i.e., not fake) but lack the perceived aesthetic or moral virtues needed to be classified as ""authentic,"" or, conversely, to have its authenticity challenged. Thus, we can have marketplace conditions where authenticity, in its full moral and aestheticized sense, is not a relevant cultural category.To illustrate, barring extenuating circumstances, consumers seldom venerate conventional mass-produced goods (e.g., a Big Mac, a Gillette disposable razor) for their authenticity because they lack potent associations with rarefied aesthetic ideals. Conversely, such items are not typically classified as inauthentic either (assuming that they are not knock-off products), with companies often promoting the standardized nature of their branded offerings—and the resulting performance consistency—as value-added benefits.In the context of the elitist critique, the ""not inauthentic + not authentic"" classification suggests that middle-class consumers who support conscious capitalist brands and enterprises are, owing to their class privileges, inherently ""not authentic."" This disauthenticating implication hinges not on conscious intent but on the systemic advantages afforded by their relatively privileged socioeconomic position. Rather than being hypocritical per se (i.e., authentic + inauthentic), the implication is that such consumers may genuinely believe that conscious capitalism is a viable means to create a more equitable and just society. However, their genuine belief is an ideological one, steeped in their internalized class interests. Middle-class consumers' ideological affinity for the market logic of conscious capitalism allows them to lead a materially privileged lifestyle in a guilt-free manner ([92]). By purchasing brands and goods that signify a heightened social consciousness (e.g., fair trade coffee; TOMS shoes; organic, locally sourced foods), they can feel symbolically absolved from culpability in the perpetuation of socioeconomic inequalities. Consequently, their habituated class predilections also create an ideological blind spot toward the exclusionary signals that conscious capitalist ideals and values convey to those who lack the economic and cultural resources needed to fully participate in a middle-class lifestyle ([47]). Inauthentic + not authenticThis ambiguous classification suggests that conscious capitalists' products, services, and brands are, to use[66] term, ""gimmicks"" that always promise more than they can deliver. [21], p. 668) further discuss issues relevant to this classification in their typology of transactions. Among their designations of deceitful transactions (i.e., scams), they list ""fraud"" and ""confidence games."" In the former condition, a disingenuous party misrepresents their intentions to an unsuspecting partner; in the latter condition, the scammer actively enrolls their target in the ruse, such as in catfishing and pyramid schemes.The inauthentic + not authentic classification implies a manipulative opportunism whereby an unethical agent feigns genuineness to extract ill-gotten gains from another. In the context of conscious capitalism, this disauthenticating association is most germane to the marketer side of the exchange. As one well-known example, the business ethics journalist Jon Entine accused the pioneering conscious capitalist brand The Body Shop, and its founder Anita Riddick, of fraudulent misrepresentation. According to [25], Riddick stole the brand concept from a local entrepreneur and fabricated an authenticating origin story about traveling the world searching for natural skin care and hair treatments. His exposé further contended that The Body Shop significantly overstated the percentage of profits that it donated to philanthropic causes. Though Riddick formally denied these charges, the authenticity challenges posed by these accusations, as well as others that subsequently followed, continued to plague the brand ([76]). After years of underperformance, relative to the brand's prescandal pinnacle, The Body Shop undertook a revitalizing strategy that its management characterized as an activist revamp ([77]).The ""inauthentic + not authentic"" classification can also cast more nuanced doubts on the authenticity of conscious capitalist entrepreneurs. Although such conscious capitalist entrepreneurs would not be committing overt acts of fraud (i.e., they are not lying about their business practices per se), the disauthenticating implication is that they are cynically espousing higher-order civic ideals to serve commercial ends, such as charging a premium to their consumers or driving higher stock valuations. This disauthenticating association can arise, for example, when the founder/chief executive of a privately owned conscious capitalist brand sells its rights to a larger corporate entity. Such a backlash arose when Gene Kahn—the founder of Cascadian Farms—sold his business to General Mills. Many leading voices in the organic food community lambasted Kahn's integrity, condemning him as a Boomer sellout and warning that the brand's corporate ownership would not stay true to the higher-order values that originally galvanized the organic food movement (see [75]). Research ProceduresTo investigate how Slow Food advocates negotiate the elitist critique of conscious capitalism and its disauthenticating connotations, we recruited informants from a Slow Food chapter located in a metropolitan area of the Midwestern United States using informational flyers, contacts made at local chapter meetings, and snowballing referrals. We conducted interviews at public locations such as coffee shops or at Slow Food–sponsored events, with exception of two that respectively occurred in these participants' domestic residence and private work office. Interviewees were paid $20 in appreciation for their time. Interviews were audiotaped and ranged from one to four hours in duration, yielding 830 double-spaced pages of verbatim text. All participant names are pseudonyms.Of our 19 interviews, 8 were conducted with chapter organizers, 5 with Slow Food producers and entrepreneurs, and 6 with Slow Food advocates who had volunteered their time to different outreach activities (for our participants' profiles, see Table 1). Most of our Slow Food organizers and consumer advocates are college graduates employed in professional occupations and hail from middle- and upper-middle-class families. Among the entrepreneurs, Dave, Leslie, Maggie, and Tom are also college graduates. This demographic profile matches the membership ranks of Slow Food USA, which skews toward middle-class professionals ([16]).GraphTable 1. Participant Profiles. 1 Notes: M = male; F = female; B.A. = bachelor of arts; B.S. = bachelor of science; M.A. = master of arts; MBA = master of business administration; M.S. = master of science; Ph.D. = doctor of philosophy.Following the conventions of in-depth phenomenological interviewing ([85]), our participants largely determined the course of the dialogue. The interviewer relied on follow-up probes to elicit more detailed accounts of the informants' experiences and viewpoints and to ensure that various aspects of food production, distribution, and consumption were covered. Procedurally, our interpretation developed through an iterative process of creating, challenging, and reworking provisional understandings by tacking back and forth between individual transcripts and the broader data set ([84]). We then pivoted to another level of hermeneutic tacking that entailed iterations between these emic narratives and theoretical concepts, which led us to the application of the semiotic square and our resulting focus on the elitist critique and corresponding strategies for countering the authenticity challenges posed by the cultural contradictions manifest in this market system. Contextual Background The Slow Food MovementAs an institutional entity, Slow Food is a transnational organization encompassing 1,500 local chapters plus numerous subsidiary organizations. Beyond its formal institutional boundaries, Slow Food's culinary practices, values, and activist goals organize ideological and economic alliances among a globally diffused network of food writers (such as Mark Bittman and Michael Pollan), consumers, producers, merchants, and restauranteurs (including celebrity chefs Alice Waters and Jamie Oliver). As [18], p. 131) writes, ""The phrase 'slow food' strikes a chord among the public not because it is the name of an organization but because it reflects a series of desires, interests and concerns.""Slow Food discourses valorize meals that are traditionally prepared with fresh ingredients as unique sources of pleasurable experiences that can mobilize consumers to resist the industrialized system of food production. Over the years, the Slow Food movement has embraced a broader conscious capitalist agenda that advocates for sustainable production, environmental protection, and social justice (i.e., fighting hunger, advocating for living wages for agricultural workers; see [16]; [89]). The Elitist Critique of the Slow Food MovementLike other conscious capitalist exemplars, Slow Food has also been plagued by charges of elitism from its inception in 1986 when its founder, Carlo Petrini, organized a series of public protests over the opening of a McDonald's in the heart of Rome (see [89]). This ignominious view of Slow Food finds ready expression in both academic analyses (e.g., Guthman 2007; [56]) as well as journalistic accounts, such as [38], who states that ""none of the aggressive, judgmental pitches of the movement have ever been proven. The power of its association with the economic elite has.""From this skeptical standpoint, Slow Food's exalted rhetoric of sustainable diets, biodiversity, and socially conscious eating (see https://www.slowfood.com/about-us/our-philosophy/) is a guise for privileging upper-middle-class tastes over the dietary practices of less affluent (and lower-cultural-capital) consumer segments (see [53]; [56]). Even Slow Food's ardent proponents, such as food writer Annie Levy, concede that a tacit elitism has hindered the cultural diffusion of its core principles:—The revered Alice Waters once said, ""when we eat food that is fast, cheap, and easy, we digest those very values."" What are the judgments contained in this kind of statement? She intends, I believe, to critique the values of a food system that doesn't care about its conditions or effects on people and the environment. But the words suggest that if you eat fast, cheap, and easy you become fast, cheap, and easy—language many women might recognize as shaming. Isn't this how it really sounds to someone who enjoys such food, or is caught in situations in which it might seem the best available option? ([58])Slow Food encourages consumers to shift their culinary tastes away from fast food and industrialized fare (including the oft-demonized category of junk food) ([16]; [83]). Such admonitions can imply a moralistic hectoring and an invidious comparison with those whose food tastes and practices are more orthodox. These elitist associations often cross into other sociocultural spheres, such as the controversy sparked by former First Lady Michelle Obama's school lunch initiative, which was institutionalized through the Healthy, Hunger-Free Kids Act of 2010. By explicitly disavowing fast food and processed foods, the revised school lunch guidelines dovetailed with Slow Food's mobilizing agenda—an alliance that Slow Food USA was eager to promote (see Figure 2).Graph: Figure 2. Fodder for the elitist critique: Slow Food's controversial political alliances.Once these Slow Food–friendly standards went into effect, news (and social) media began to feature anecdotal reports of children refusing to eat these presumably unpalatable lunches and skyrocketing food waste ([28]), with some critics characterizing the program as ""gastro-fascism"" ([71]). The elitist charge became integral to this cultural (and political) backlash:Michelle Obama thinks she knows what your children should eat. She's adamant about promoting her nutrition policies for kids, even the new and disastrous school meal standards implementing the ""Healthy, Hunger-Free Kids Act.""... But attending Ivy-League schools doesn't magically make someone better parent material than an individual who attended a public university, or, dare it be said, someone who didn't attend college. ([ 4])Like other market exemplars of conscious capitalism, Slow Food's aesthetic and experiential arguments have become strongly associated with an unwarranted moralism (i.e., the authentic + inauthentic classification; primary relation of contrariety). Slow Food advocates frequently argue that fast food is a debased cuisine that deprives humanity of meaningful and rewarding experiences of eating and sociability ([73]; [83]). For the many consumers who have warm memories of enjoyable fast-food meals with friends and family (and maybe look forward to such treats), Slow Food's moralizing pronouncements seem to emanate from an elitist taste bubble that is disconnected from everyday pleasures and real-world practicality. Similarly, Slow Food's veneration of locally sourced ingredients, heirloom vegetables and grains, artisan-crafted foods, and seasonal cuisine also seems to assert an unwarranted claim to moral virtue. Rather than sacrificing for a greater societal good, such rarefied culinary objects seem more attuned to signaling that one possesses the discretionary resources of time and money to treat food and cooking as a self-actualizing identity practice. Accordingly, Slow Food is easily, via the elitist critique, decried as an aggrandized form of cultural snobbery (e.g., [53]). Authenticating Strategies in the Slow Food MarketFigure 3 represents the correspondences between Slow Food's contextualized authenticity contradictions, the disauthenticating association that ensues from each contradiction, and the strategies through which Slow Food advocates seek to negate these authenticity challenges. In this representation, indexical authenticity (authentic ↔ not inauthentic) is the contested ideal that our Slow Food advocates are seeking to defend.Graph: Figure 3. Authenticity contradictions and authenticating strategies in the Slow Food market.Our Slow Food consumers place the most emphasis on the reflexive strategy, which they use to counter the authentic + inauthentic contradiction (primary relation of contrariety) and its disauthenticating association of virtue signaling and moral pretentiousness. Rather than rejecting the elitist critique outright, Slow Food advocates interpret it as a warning sign that the Slow Food market has become a gentrified facsimile of the movement's origins in the everyday cuisines of rural Italians (i.e., a disparaging version of iconic authenticity). Accordingly, our participants revere practices that seem to resurrect Slow Food's agrarian values and democratizing goals.The humanistic rebel strategy redresses the not inauthentic + not authentic contradiction (secondary relation of contrariety) and its disauthenticating association of social exclusion. In the context of the elitist critique, this contradiction holds that individuals whose lives have been shaped by class privilege may be blithely unaware of their own internalized elitist predispositions. From this standpoint, Slow Food advocates may have a genuine interest in making the world a better place (i.e., they are not consciously ""faking it""; rather, they are being ""not inauthentic""). However, they are largely oblivious to how their viewpoint on these problems and solutions has been shaped by a life of class privilege and their habituated, middle-class (bourgeoisie) sensibilities. This disauthenticating association renders Slow Food consumers as being somewhat akin to the proverbial fish in water. Rather than not realizing they are wet, however, the analogical implication is that they cannot comprehend that other terrestrial animals lack the requisite resources to enjoy life in the water, as they do.To negate this authenticity challenge, our participants drew from humanistic rationales, such as the idea that certain kinds of experiences and social connections have magical and transformative qualities that transcend social differences ([ 2]). Importantly, this strategy combines a humanistic ethos with the idea of rebelling against a deleterious marketing and cultural status quo and, thereby, creates a distinction to the complicit, part-of-the-problem connotations of liberal elitism (see [92]).The perfective strategy corresponds to the inauthentic + not authentic contradiction (relation of complementarity). This strategy is most relevant to those positioned on the entrepreneurial/production side of this market system. It aims to negate the disauthenticating association of commercialism (i.e., conscious capitalist enterprises are profit-seeking marketing ploys). In response, our Slow Food producers and entrepreneurs draw from the bohemian ideal of the artist who refuses to compromise their artistic vision, despite market incentives to ""sell out"" (i.e., betraying one's artistic integrity in return for financial reward) ([10]; [87]). Accordingly, they present themselves as being intrinsically committed to perfecting their Slow Food craft and pursuing conscious capitalist values and ideals, rather than doing it for the money. The Reflexive StrategySlow Food advocates use the reflexive strategy to negate the authenticity challenge of moral pretentiousness. The implication is that Slow Food assigns an unwarranted degree of moral virtue to those who have the economic wherewithal to buy rarefied ingredients, spend time on complex meal preparations, and dine at expensive farm-to-table restaurants while casting those who lack such resources as less virtuous consumers. In response, our participants interpret Slow Food's cultural associations with affluent foodies and elite taste practices as a regrettable, but correctible, market distortion of the movement's authentic values and practices.While acknowledging that market upscaling has imbued Slow Food with an elitist aura, our participants reiterate that expensive, epicurean cuisine need not be and, indeed should not be, regarded as the quintessential expressions of Slow Food:I think one of the things is this perception that if you shop at farmers' markets or at the co-op, it's a lot more expensive. And there is a little bit of this Slow Food bent into cooking elaborate meals, and I think some people perceive that as being elitist because it's sort of this educated way of thinking about food. I don't think of it as being elitist because a lot of times, recipes can be super expensive to buy all the ingredients for, but they don't have to be. I don't think that enjoying your food should be something that is thought of as elitist.... Like, I buy what's not super expensive at the co-op and I cook pretty simply.... What I really like about Slow Food in particular is the aspect of enjoyment and that good food is for all—what good, fair, clean food means for the farm worker to the people who are consuming the food. (Erin)Erin does not summarily dismiss the elitist charge. Rather, she takes a more ambivalent stance by first conceding that Slow Food values and ideals are often enacted in ways that can be read as elitist, such as cooking elaborate meals using expensive ingredients. In her authenticating interpretation, she counters that Slow Food values are better expressed through fundamentally simple meals that do not require extensive preparation time or costly ingredients. Her emphasis on there being many affordable options at her food co-op (which is, of course, a relative judgment) and on ""cooking simply"" (another relative assessment) convey that she is staying true to Slow Food's core principles rather than trying to place superficial, foodie predilections on a higher moral plane.Erin further counters this aspect of the elitist critique by incorporating the economic interests of farmers into her inclusive interpretation of Slow Food stakeholders. This interpretation creates a rhetorical contrast between Slow Food's foundational discourse of economic populism (emphasizing fair wages for agricultural workers) ([73]) and the elitist condemnation that higher prices are merely a means for affluent consumers to mark status distinctions.Paula's narrative exhibits a similar authenticating logic to that expressed by Erin:Slow Food has often come under fire for being elitist. I don't actually think that's true.... The beginnings of Slow Food were about people eating good food, and those were not necessarily rich people. We are talking about people who might have had very little money.... When most people think about amazing Italian cuisine, they were eating very basic foods. So, the whole idea of eating good food to me doesn't seem elitist at all.... Slow Food in the United States, yes, we do certain things that might be seen as elitist—the restaurant dinners and stuff like that. But again, you are still educating people. You are still getting more people involved. And the more people who know about local farming, sustainable farming, eating seasonally, making sure that farm workers are protected and paid properly, that spreads out. And we do projects with a variety of different populations, and we are trying to do more of that.... Slow Food does a lot of work in all its chapters to help with urban gardens or school gardens.... In the long run, our goal is that all people have access to this kind of food.... We are working toward passing that power on to more people. So, I don't think wanting children and families in need to have high-quality food is elitist. (Paula)In this vignette, Paula first differentiates Slow Food practices from elitist pretentions by invoking its historical connections to rustic Italian foodways. She asserts that Slow Food enjoins a pleasurable, resourceful, and fundamental relationship to food that should be accessible to people from all walks of life, rather than being an exclusive province of affluent consumers. However, Paula also recognizes that her inclusive rendering of Slow Food is contradicted by the realities of socioeconomic stratification. From Paula's viewpoint, Slow Food's community outreach efforts can play a pivotal role in democratizing these forms of culinary cultural capital so that consumers from less privileged backgrounds can acquire the skills and knowledge needed to incorporate Slow Food practices and ideals into their culinary routines.When utilizing this reflexive strategy, Slow Food advocates routinely assert that cultural capital ([ 9]), rather than a lack of economic resources per se, is the primary barrier that keeps consumers from integrating Slow Food ideals and practices into their everyday lives. Christina echoes this rationale when discussing how low-income consumers could enact Slow Food practices if they had more knowledge about utilizing the fresh produce and bulk goods that often go to waste in the local food pantry where she volunteers:Some people who are in Slow Food are foodies. However, it does not cost a lot of money to eat right. There are food pantries who throw away produce because people who come to the pantry don't know what to do with it and they don't take it.... Fresh produce going to waste.... No one wants it because they don't know what to do with it. It's really unfortunate. So, people have more access than they think. There are bulk aisles at grocery stores that you can get food for less money. It is actually a lot less expensive to buy bulk rice or bulk oats or whatever else, than to buy the bagged, boxed stuff that's like creamy preprocessed. I think the real lack of resource is education, not so much money. (Christina)Slow Food advocates often draw an authenticating contrast between foodie-ism—which fetishizes highly aestheticized meals prepared with exotic (and typically expensive) ingredients (see [52])—and the Slow Food ethos of preserving traditional foodways and skills ([75]). This distinction is quite salient to Slow Food chapter leader Kevin. He posits that Slow Food's culinary values and ideals have become misconstrued in their translation to the consumerist, status-conscious culture of the United States. Kevin's goal is to reclaim Slow Food's original ethos from its commercial appropriation by high-end retailers and restaurants:To buy imported cheese, organic wine, and all these kinds of things, I don't think those are meant to be the most obvious expressions of Slow Food values. And I think this is where the cultural translation from Italy to the United States went wrong, is that it got tied up with those folks [i.e., affluent foodies]. In Italy, it's much more about cooking at home. It's much more about preserving grandma's recipes. It's much more about celebrating the seasons and the tradition and preserving home ways of life than it is about eating in restaurants that do everything right. And you know, like anything else, capitalism wants to subsume this revolution.... That's a schism that I am personally trying to address and maybe lead by example. I don't think we should be cooking like a Michelin-starred restaurant at home. I think we should be cooking like our grandparents and great-grandparents. And I think we can learn a lot from traditional cultures and indigenous people—to the extent that there still are any indigenous people—how to eat well, and you know, a lot of those foods have become an affectation in restaurants. They'll have poutine, but it's made with truffles, and confit duck and elaborate things. I have realized that we are all attracted to the comfort foods and the simple foods, like tacos, and they are easy and fun to make. (Kevin)For Kevin, Slow Food should be accessible, basic, fun, and easy—characteristics that diverge from associations with rarity, cosmopolitan sophistication, aesthetic refinement, and technical proficiency that mark elite tastes ([44]). If read in a more critical light, Kevin's narrative reiterates the nostalgic glossing of preindustrial foodways that critics of the Slow Food movement assail for ignoring the harsh realities of scarcity and subsistence endured by those who had to survive on ""traditional diets"" ([56]).While romanticizing images of a bucolic culinary past have considerable appeal to our Slow Food advocates, the idea of rekindling a premodern utopia is not that central to the reflexive strategy's authenticating function. Rather, these homages to a bygone era, when people lived close to the land and prepared food in traditional ways, symbolically link Slow Food practices to agrarian and/or rural lifestyles far removed from elite pretensions:I did an internship through Worldwide Working on Organic Farms.... I went to Italy and I milked sheep and goats for a couple of months. And it was very rural. It was very low-tech. We milked in buckets by hand, sheep and goats, and we kind of went out with big sticks and sheepdogs and herded them.... Slow Food originated in Bra, Italy, and that was only like an hour and a half away from the farm. So, I think that's kind of how Marco [the farmer] was involved in Slow Food. He made cheese that was very well regarded, and he went to cheese festivals and stuff. But I mean the whole day was slow. Like wake up kind of late, drink your espresso, milk leisurely, walk the mount, you know. Dinner took a really long time, but that was kind of okay. And just kind of do the same things over and over again. So, we all cooked together. They also did some kind of agro-tourism. They'd have people from the city come out and we would cook with them the food we either grew or found.... That was fun. (Leslie)Leslie's narrative validates a nexus of Slow Food ideals regarding small-scale production and the slower pace of agrarian life. While this rural setting has some trappings of a staged performance—most notably Marco's side business in agro-tourism—it places Slow Food in a symbolic sphere far removed from the Whole Foods brandscape, expensive farm-to-table restaurants, and other consumption domains invocative of foodie affectations (see [52]). For Leslie, her story of interning on a rustic Italian farm affirms that she has actually lived the conscious capitalist values she endorses through her Slow Food advocacy (and thus is not a hypocritical moralizer). More generally, consumer narratives that link Slow Food practices to traditional modes of food production, down-home family meals, and simpler ways of living—rather than exorbitantly priced gourmet dishes—express a rhetorical parry to the elitist charge of moral pretentiousness. The Humanistic Rebel StrategyOur Slow Food advocates use the humanistic rebel strategy to redress the authenticity challenge posed by the elitist critique's connotation of social exclusion and the related sociological argument that consumers' social class backgrounds structurally predetermine their taste affinities ([44]). From this critical viewpoint, Slow Food advocates may not consciously intend to be elitists, but their preferences for goods that convey meanings of sustainability, locavorism, and artisanship betray a host of class advantages that distinguish them from consumers whose lives are marked by conditions of necessity ([47]). While Slow Food advocates may be well intentioned (i.e., they are being ""not inauthentic""), they are also complicit in a system of institutionalized class-based inequities.To illustrate this tension, let us reassess Leslie's preceding vignette in relation to this association with social exclusion (rather than moral pretentiousness). On the one hand, working for room and board on a small, rural farm clearly diverges from conventional notions of status posturing. However, a sociological counterpoint is that Leslie—as a college-educated young adult engaging in an exploratory experience—is building a reservoir of life stories and cultural capital that can afford career and status advantages later in life (see [91]). Seen in this sociological light, Leslie is enacting her class privilege by having the economic and social latitude to intern on a rustic, Italian farm before transitioning into more conventional middle-class occupational pursuits, such as attending graduate school.As a chapter leader, Kevin has oriented his local chapter's activities toward the goal of making Slow Food a more inclusive organization that does not merely cater to the interests of middle-class consumers. When implementing these outreach projects, however, Kevin recognizes that many Slow Food practices are simply incompatible with the situational demands that lower-income consumers have to negotiate on a daily basis:I have amazing privilege,... like being an American to a middle-aged white guy who has very marketable skills.... But I realize that that is a privilege, and this is the biggest thing for us in Slow Food to grapple with—that a single mother who has three jobs and two kids doesn't have the luxury of deciding, ""I think I would like to work less and spend more time in my garden"".... So, you have to be very careful and sensitive about it. (Kevin)Kevin believes that this class chasm can be bridged if his team of middle-class volunteers presents Slow Food's approach to food provisioning, cooking, and eating in a manner that is ""sensitive"" (i.e., adapted) to the lifestyle constraints faced by less well-resourced consumers. His optimistic viewpoint is based on the authenticating assumption that Slow Food's experiential and social benefits have an inherent appeal to consumers, regardless of their class position, because they tap into fundamental human desires and needs.The humanistic rebel strategy takes this inclusive rationale further by suggesting that a confluence of technological and commercial forces have locked individuals into an accelerating pace of life. Consequently, experiences of social connection, spontaneity, and everyday small pleasures are sacrificed to demands for efficiency, convenience, and the seductive (and ultimately alienating) effects of social media and digital communications.Aaron echoes this humanistic rebel mantra in his commentary on the inherent importance of eating and cooking with others:You have the social component of Slow Food as cooking and eating together and taking time to reflect and to connect and to develop community. That's something that we lose when we have things like drive-through or microwave dinners, which aren't to be destroyed or demonized altogether. I take advantage of these services of society. But for them to be the baseline means that we are losing what ... enriches our social systems a lot more than people eating alone and interacting through screens.... So, I think food, when it's jointly cooked and eaten, serves as a very natural medium for connection and idea generation and creativity. (Aaron)Aaron characterizes Slow Food as a needed corrective to societal transformations in the practices of cooking and eating that have resulted in a loss of sociability, communal bonding, and creative interactions. By interpreting Slow Food as a medium for enjoining meaningful social interactions, Aaron expands its cultural province well beyond the predilections of affluent consumers. Aaron's caveat that he has partaken in the conveniences afforded by fast food and microwave meals is another rhetorical means for distancing his Slow Food preferences from elitist connotations. His narrative signals a conscious disavowal of snobbery by acknowledging that there is a legitimate time and place for ""fast food."" Aaron then specifies the problem as the cultural ubiquity of fast food, which, in turn, he links to dehumanizing consequences.When expressing the humanistic rebel strategy, our participants often couched the communal experiences of preparing or eating food as magical moments that affirm Slow Food's class-transcendent qualities:I ran a cheese-making class, and that was really fun because I've always been interested in cheese-making, just for the fun of it. There were seven or eight people there. And one of the members still is making cheese today. And it was really fun to be able to share that magic with people. That this is how this cheese actually comes into being, and it's totally doable by yourself at home. So, that's really exciting, that he got so inspired. It's fun to see somebody get really interested in something. (Amanda)Amanda emphasizes the magic of cheese-making and the inspiration and rewarding personal experiences of self-sufficiency it can enjoin (i.e., ""totally doable by yourself at home""). Her narrative expresses a cosmological view of nature as a magical, life-transforming force ([ 2]) whose authenticating properties are not inherently tied to class-shaped tastes. In keeping with this formulation, Amanda interprets the sharing of her Slow Food skills as a means to help others experience these inspiring connections to nature, which, in turn, implies a revelatory contrast to the alienated experiences of industrialized fast food.Maggie similarly interprets her self-taught Slow Food skills as a means to help people create a sense of communal togetherness and to experience new sensory pleasures and magical connections to the land:I think of Slow Food as taking your time to respect the ingredients and preparing them from scratch and enjoying food. And that, to me, resonates. And bringing back the social aspect of eating. Like, you take time to prepare this meal, you sit down, you share it with people who care about the same things that you do. And it's also, creating another community of people who value these things whether they're growing, or cooking, or eating; having that kind of common thread, I think is really satisfying. (Maggie)Maggie venerates Slow Food as being inherently conducive to experiences of sociability and community and as a way to recognize the meaningfulness of food (a normative orientation that reads as a general human value, rather than a class-interested practice). However, Maggie is a meat producer who, like other Slow Food entrepreneurs, faces an additional task of negating contradictions that derive from the ""inauthentic + not authentic"" category. The Perfective StrategyThe perfective strategy seeks to negate Slow Food's disauthenticating association with commercialism. This aspect of the elitist critique casts Slow Food producers and entrepreneurs as disingenuous actors who are enrolling consumers into an inauthentic market relationship (akin to a gimmick or a confidence game) to serve their own economic interests. In response, our Slow Food entrepreneurs strive to authenticate their actions by signaling that they would never compromise their Slow Food ideals for the sake of profit, such as by recounting the copious amounts of time and energy they invest into perfecting their Slow Food enterprisesIn this spirit, Tom, a farm-to-table restauranteur, views his business as a way to enact his passionate commitment to producing food in a more meaningful and socially beneficial way:When you go to a fast-food restaurant, you have no idea of who actually made that food and the process of where it came from is not known to you. The taste and flavor are mostly engineered to play off the cheap sensory sensations. So, it's fatty and salty and sweet and so, yeah, on a certain level, it might be gratifying, but it's a cheap way to do it that is less meaningful. Slow Food is like, ""We're going to do things in a way that is process oriented!"" I talk about process a lot.... We [Tom and his restaurant staff] were really structured around learning, and so it was a process where we feel like we've excelled and learned a lot and we'll keep pursuing that.... [With Slow Food,] you have this process where people are eating and making something and understanding where it came from and how it works. Eating is such an important part of our lives, and it can have a really important impact on our community and environment. So, the more you understand about it, hopefully you'll make better decisions. The basic motto of Slow Food is clean, fair, and good food. I can totally get behind those values. (Tom)During his interview, Tom extensively discussed ""the process"" aspects of his restaurant and how he views it less as a business than a means to cultivate and diffuse knowledge about the complex interrelationships among food, cooking, sensory enjoyment, ecology, and societal well-being. His quote also reiterates Slow Food's argument that the experiences of these simple culinary pleasures can mobilize consumers to resist the industrialization of the food system (thus echoing the humanistic rebel strategy). For Slow Food entrepreneurs, however, it serves the additional function of associating their enterprises with civic goals that stand distinct from conventional commercial aims.Returning to Maggie, she raises pasture-fed rabbits for sale to farm-to-table restaurants and consumers. In developing her production techniques, Maggie has constantly experimented with different procedures and equipment designs. Through this long trial-and-error process, Maggie believes she has developed an innovative method that better simulates the lives her rabbits would enjoy outside of captivity:Maggie: Daniel Salatin is the son of Joel Salatin, who is the owner of Polyface Farms, and he is the person who is raising rabbits in this system that he has devised and calls the Hare Pen system. So essentially, you still have your does in cages.... You put them in a glorified cage that you then put on grass.... I've copied their system exactly, and I was very unsatisfied with the results that I got. [Maggie then provides an extensive description of her alternative and labor-intensive system and how she developed it]... I don't know why I kept doing it. But I finally have a system that is really effective.... It just was a lot of observation of the rabbits on pasture, making so many mistakes and then incorporating what I had learned.Interviewer: Did you have any economic incentives?Maggie: No! It has to be a personal belief that there might be a better way to do things.... It's kind of like what makes an artist a good artist. If they all hold the brush the same way and they are using the same colors, but they create vastly different things, and one appeals to you, and one doesn't appeal to you. So, what makes that one piece of art recognized by the vast majority of people as superior?... I have this wonderful platform to invest energy and creativity, and it's nice. And so, I feel in some ways really lucky.Invoking the image of the passionate artist, Maggie distinguishes her efforts to perfect an ecologically appropriate system for raising rabbits from crass commercial and economic interests. Maggie's closing sentiment expresses her authenticating belief that such actions can make things better, rather than being driven by instrumental aims. Through storytelling, and by showing how her system works to customers who visit her farm, Maggie deploys narrative and material resources to negate disauthenticating concerns that her Slow Food affinities are merely an instrumental means to charge higher prices. Her personal investment in learning about rabbits' natural habitats/behaviors and inventing a complex ecosystem for raising them further signals that she is not likely to compromise her Slow Food principles in the interest of commercial expediency. Discussion Theoretical ImplicationsPrior research has treated authenticity as a perceptual value or quality that consumers attribute to a brand ([11]; [55]), person-brand ([87]), product ([57]), or performance ([ 5]; [39]). In contrast, we have reconceptualized authenticity as an ongoing process through which consumers and marketers negotiate a contextualized system of cultural contradictions and ambiguous classifications. We suggest that our semiotic framework can better analyze the authenticity contestations that arise in a given market or sociocultural context than conventional theories that assume authenticity perceptions operate on a continuum or selectively draw from an essential set of defining attributes.The conceptualization of authenticity as a relative point along an authentic-to-inauthentic continuum ([22]; [65]) can depict a zone of ambiguity where the authenticity or inauthenticity of a market actor is perceived as being uncertain and, thus, debatable. However, this conceptualization does not offer a means to specifically analyze the cultural meanings (and the interrelationships among them) that generate these ambiguous perceptions. Accordingly, it offers limited theoretical discrimination and managerial guidance.For example, [65] argue that quality commitment, heritage, and sincerity are the primary perceptual cues of authenticity. They then propose that brands should differentially leverage these cues depending on whether consumers perceive them as having low, moderate, or high levels of authenticity. In their normative framework, brands with low perceived authenticity should emphasize sincerity, brands with moderate perceived authenticity should emphasize quality and heritage, and brands with a high level of perceived authenticity should emphasize all three authenticity cues.Such recommendations presume that brands falling into the lower and middle sectors of this proposed continuum have a shortfall of perceived quality commitment, sincerity, or heritage that is rectifiable through compensatory signaling. However, such contested brands are often plagued by contradictory meanings that undermine their promoted claims to authenticity ([37]; [86]). Furthermore, more complex, disauthenticating narratives, such as the elitist critique, can cast doubt on the very credibility of such authenticating cues when used by a contested brand or actors in a market system.Turning to combinatory definitions, [67] have offered a comprehensive theorization of authenticity (as understood from the consumer's perspective) that warrants comparison to our approach. They identify six subdimensions of authenticity (accuracy, connectedness, integrity, legitimacy, originality, and proficiency) and then trace out the relative impact of those dimensions across different market categories and on consumers' behavioral intentions. Rather than a continuum, Nunes, Ordanini, and Giambastiani argue for a family resemblance explanation in which ""a concept (authenticity, in this case) may be qualified by different subsets of its dimensions across different contexts, and not always by all of them in the same way"" (p. 16).Like a continuum, [67] family resemblance logic is limited to the explanation that authenticity is a multidimensional construct whose subcomponents may be more or less important in a given market or consumption context. In contrast, a semiotic framework shifts attention from correlational premises (e.g., this authenticity subdimension seems more important for hedonic products than utilitarian ones) to the cultural meanings, and underlying structural contradictions, relevant to a particular judgment regarding the authenticity of a given product, brand, or market action. For example, the authentic ↔ inauthentic tension elevates the importance of authenticity's moral dimensions in ways that traverse product category distinctions, such as hedonic or utilitarian.To illustrate, a hamburger would typically be classified as a hedonic good. [67], pp. 3–4) find that judgments of ""legitimacy""—which they define as ""the extent to which a product or service adheres to shared norms, standards, rules, or traditions present in the market ... appear to matter for utilitarian but not hedonic products."" However, if we examine this consumer choice in the context of the Slow Food market, then legitimacy becomes a far more important issue. From this standpoint, an ""authentic burger"" would need to exhibit fidelity to various aesthetic and moral norms—grass-fed beef, local sourcing, traditional preparation techniques, and so on—and, its perceived authenticity would be understood and legitimated through a contrast to fast-food burgers. That authenticating contrast (the fast-food burger vs. a Slow Food burger) could then become subject to the elitist critique, which, in turn, would provide motivation for Slow Food advocates to negate these disauthenticating associations.In summary, we have argued that authenticity is culturally constructed (and contested) in a network of structural relations (rather than being a discrete set of essential properties attributed to a brand, person-brand, market performance, or market relationship). Consumers and marketers alike covet indexical authenticity (i.e., the abstract ideal of authenticity) because it can confer cultural legitimacy ([51]), moral authority ([59]), and identity validation ([ 7]; [86]) all of which, can be converted into micro-celebrity status ([79]) and a branding asset ([31]; [45]). However, this authenticity ideal is structurally linked to contradictory meanings and ambiguous classifications. When consumers' or marketers' authenticity claims are challenged by these cultural contradictions, they have pressing incentives to distinguish their actions and identities from the invoked disauthenticating associations. In the following subsection, we discuss how this authenticating goal can be enacted by negating associations that flow along the contradictory path of deception and promoting those that follow the contradictory path of redemption. Two Managerial Paths to Authenticating a BrandAs [49] have argued, marketing managers often find it difficult to redress brand image problems because they are unable to effectively decipher the cultural meanings contributing to those dilemmas. Our semiotic framework can help redress this managerial shortfall. It offers a tangible means for marketing managers to systematically analyze the cultural contradictions of authenticity that emerge in a given market and then to identify strategies for authenticating their brands in the face of these challenges.As a general heuristic, we propose that marketers can be successful in authenticating their brands and/or other strategic assets when they are able to accomplish two complementary goals. The first is to leverage cultural meanings that negate the disauthenticating associations that flow along the contradictory of deception path (authentic → not authentic; see Figure 1). When consumers follow this perceptual path, they experience a glaring contradiction between a prevailing ideal of authenticity and its market manifestation in a brand or marketing practice (authentic ↔ not authentic), which then leads to an association of inauthenticity via the complementary relation of not authentic → inauthentic. In response, marketers should try to provide consumers with compelling and emotionally resonant meanings and rationales that discount the credibility, relevance, or importance of the disauthenticating associations that have gained cultural currency in their respective market.As one illustration, Patagonia confronted a path of deception authenticity challenge soon after it began campaigning against the Trump administration's executive order to reduce the size of Utah's Bears Ears National Monument by two million acres. On December 4, 2017, Patagonia featured this message on the front page of its website: ""The President Stole Your Land: In an illegal move, the president just reduced the size of Bears Ears and Grand Staircase-Escalante National Monuments. This is the largest elimination of protected land in American history."" This web page then directed consumers to various information sources and encouraged consumers to contact their elected officials and to also take the protest to social media, using the hashtag #MonumentalMistakes (see [ 1]).However, defenders of the administration's policy change were quick to denigrate Patagonia's activism as a deceptive marketing ploy. Interior Secretary Ryan Zinke condemned Patagonia as a dishonest ""special interest"" and proclaimed it was ""shameful and appalling that they would blatantly lie in order to put money in their coffers."" Utah Representative Bob Bishop, then chairman of the House Natural Resources Committee, also evoked the elitist critique in his tweet proclaiming that ""Patagonia is Lying to You... A corporate giant hijacking our public lands debate to sell more products to wealthy elitist urban dwellers from New York to San Francisco"" (quoted in [35]).In terms of our model, the Trump administration's response challenged the authenticity of Patagonia's mobilizing campaign by impugning its motivations, thereby reframing an ostensibly authentic (conscious capitalist) action as a disingenuous public relations stunt designed to extract more profits from elite consumers (authentic → inauthentic), which, in turn, triggers the complementary association to inauthenticity. In response, Patagonia joined as a coplaintiff with five Native American tribes and several nonprofit groups in a lawsuit aiming to halt the policy change ([35]; see also https://www.patagonia.com/stories/hey-hows-that-lawsuit-against-the-president-going/story-72248.html). Patagonia also continued to be a vocal critic of the Trump administration's environmental policies and, in a politically and ideologically related vein, donated all its tax savings from the Trump-backed corporate tax cut to environmental groups while condemning the new corporate tax rates as being irresponsible ([63]). Through these responses, Patagonia signaled a deeper commitment to its conscious capitalist values and gave consumers reasons to doubt or dismiss the disauthenticating associations of greed and deception that were being cast on it. In response to Patagonia's uncompromising stance, Inc. offered the following commentary on its 2018 Company of the Year finalist:For Patagonia and its fans, that purpose is doing whatever they can to try to save the planet. In 2018, Patagonia proved that it will not only preach that mission, it will do so with a much louder voice than most other companies. And—so far, anyway—it's only further burnished the Patagonia brand. ([ 8])The second marketing goal is to create conditions in which consumers interpret a brand or business along the contradictory of redemption path (inauthentic → not inauthentic) (see Figure 1). This redemptive chain of associations begins with the widespread cultural view of marketers as inauthentic and, thus, untrustworthy actors ([45]; [66]; [67]). Accordingly, we can assume that consumers will typically harbor varying degrees of skepticism and suspicion toward the authenticity of marketing and branding claims. Redemptive meanings encourage consumers to believe that a given brand or business is operating in ways that favorably diverge from the marketing status quo (i.e., not inauthentic) which, in turn, leads to the complementary relation of not inauthentic → authentic.Volkswagen's (VW's) ""Hello Light"" advertisement, which launched its new line of electric vehicles (circa 2019), takes viewers on a journey that follows a path of redemption arc (see https://www.youtube.com/watch?v=qEvNL6oEr0U). The ad begins with a silhouetted figure entering a dark and seemingly abandoned production facility, while a news report about VW's emission scandal, or ""Dieselgate,"" blares in the background. In a seemingly counterproductive marketing communications move, the ad explicitly reminds its viewers of all the inauthentic associations (VW as liar, deceiver) that arose from those ""dark"" days. The protagonist is revealed to be a despondent engineer struggling to design a new VW model, against the musical backdrop of Simon and Garfunkel's 1960s anthem, ""The Sound of Silence."" Desperate for inspiration, our engineer scours the company archives and finds his creative muse—an image of the iconic VW Van (aka the ""Love Bus"").Through the choice of song and reference to this totem of the 1960s counterculture, the ad recalls VW's countercultural legacy as an authentic symbol of antimaterialist values and a rebuke to status consciousness and marketing hype ([46]). The ad's message is that VW, despite having lost its way, still possesses a latent essential ""goodness"" that is ""not inauthentic."" As ""The Sound of Silence"" reaches its crescendo, lights go on, puncturing the darkness. We observe the production facility come to life and give metaphorical rebirth to the VW brand in the form of an electric van (which also places ""The Sound of Silence"" on a different, ecofriendly cultural register). Thus, the ad's narrative follows the redemptive path of ""inauthentic"" (VW's Dieselgate) to ""not inauthentic"" (VW's 1960's countercultural heyday) to ""authentic"" (signifying that VW has rekindled its socially conscious roots).Our discussion of the three authenticating strategies used by Slow Food consumers and entrepreneurs has emphasized their function as a defensive means to disavow or negate the disauthenticating associations that flow along the contradictory of deception path. However, these same strategies also promote affirmative meanings and associations that operate along the contradictory of redemption path. For example, the perfective strategy does more than negate the disauthenticating association with commercialism. It also magnifies authenticating differences to fast food or industrialized food production and thereby encourages consumers to interpret Slow Food enterprises in a manner compatible with the contradictory of redemption path (even though they may be aware of some disauthenticating associations). This redemptive associative chain takes the following form: Slow Food entrepreneurs are suspected to be ""inauthentic"" due to their commercial motivations → Slow Food entrepreneurs are seen as being ""not inauthentic"" because their deep commitment to artisan ideals and noncommercial values differentiates them from conventional fast-food establishments and industrialized modes of commercial food production → the signification of ""not inauthentic"" supports a broader conclusion that the Slow Food entrepreneur is an authentic actor. Implications for Managers of Conscious Capitalist BrandsGiven their shared ideological affinities, the authenticating strategies used by Slow Food advocates should also have a high degree of applicability to brands espousing conscious capitalist goals and ideals. Of the three authenticating strategies, we find numerous examples of conscious capitalist brands that have enacted some version of the perfective strategy, which aims to negate associations with commercial opportunism and foster interpretations compatible with the contradictory of redemption path. While less commonplace, we can also find branding campaigns that align with the reflexive and humanistic rebel strategies. In the following discussion, we use these various exemplars to illustrate how these authenticating strategies can be implemented by conscious capitalist brands and the authenticity contradictions they potentially redress. Perfective strategyBrands using the perfective strategy engage in unconventional actions that demonstrate a deep commitment to activist causes that supersede profit motives. Over the years, Patagonia has made frequent use of this authenticating strategy to signal that proenvironmental values were central to its corporate mission, even when such acts could mean sacrificing sales, such as its iconic ""Don't Buy"" promotion ([49]) or their ""Give a Damn"" holiday messaging ([72]). REI has also enacted a perfective strategy in its #OptOutside campaign, whereby the retailer closes its stores on Black Friday and encourages consumers to engage in a range of proenvironmental, outdoor activities. Like Patagonia, REI's campaign builds on (and authenticates) the brand's history of supporting environmental causes and promoting a heightened concern for habitat protection and environmental conservation. Last but not least, Clif Bar illustrated a fairly novel implementation of the perfective strategy when its founder and chief executive officer, Gary Erickson, published an ""advertorial"" in the New York Times, offering to donate ten tons of organic ingredients to his main competitor Kind Bars. This advertorial further promised to share his company's knowledge about organic sourcing and production so that the two companies could collectively ""lay the foundation for a healthier, more just and sustainable food system"" ([26]).Owing to their status as commercial enterprises, whose existence depends on profitability, the perfective strategies of conscious capitalist brands can always be reframed as yet another kind of commercial deception. However, such brands can lessen the cultural viability of such recursive challenges by further signaling that their passionate commitment to the supported causes takes precedence over profit motives. Though addressing a different context, [20] offer evidence that supports this strategic approach. They find that customers attribute the quality of authenticity to third-place establishments (e.g., cafes, coffee shops, restaurants) when they believe the respective proprietors are aiming to create meaningful social connections rather than merely trying to make a profit. As they write, ""The authenticity perceived in treasured commercial places is based on exchanges that go beyond mere commercial aspects.... Although being business operators, proprietors invite the consumer to engage in activities that are not undertaken purely for profit"" ([20], p. 913).Accordingly, we propose that conscious capitalist brands are more likely to be perceived as authentic when they provide tangible means for consumers to participate in their social change mission but do so in ways that are not dependent on purchases. From this standpoint, The Body Shop's repositioning of its stores as activist hubs ([77])—where consumers can listen to speakers discuss environmental and social justice issues, sign petitions, and join activist organizations—is an enactment of the perfective strategy and a culturally viable means to reestablish the authenticity of its conscious capitalist branding claims. Reflexive strategyThis strategy aims to negate the charge that a market actor is evincing a ""holier than thou"" stance for actions that are either hypocritical (e.g., ""do as I say, not as I do"") or overstate the positive impact of the self-proclaimed act of conscious capitalist rectitude. For conscious capitalist brands, this authenticating logic most readily translates into a reformist agenda. As one prominent example, Chipotle's ""Back to the Start"" campaign (circa 2012) rallied a diverse assemblage of activist groups that shared a commitment to transforming the corporate-controlled system of food production and who saw the fast-food sector as exemplifying its presumed ills (see [48]). The two-minute short film, which ran across multiple media platforms, shows an increasingly disenchanted farmer witnessing the steady industrialization of his enterprise, replete with enclosed animals, the heavy use of antibiotics, and food being transformed into nondescript goo-like substances. Against the backdrop of Willie Nelson's plaintive version of Coldplay's ""The Scientist,"" the farmer triumphantly decides to go ""back to the start"" by raising free-range animals, using traditional farming techniques, and selling his preindustrial goods to Chipotle.Some relevant insights into this campaign and its authenticating effects can be gleaned from a Fast Company interview with Jesse Coulter, co–chief creative officer of Creative Artists Agency Marketing, which worked with Chipotle's management team in developing this campaign:We were tasked to find new ways to tell Chipotle's Food with Integrity story.... The first issue Chipotle wanted to address was industrial farming.... Chipotle shared many stories of family farmers who have turned their farms into factory farms and have subsequently grown to regret it.... It was provocative because it took a stab at Big Agriculture. Chipotle is a bold company, who has the courage to really stand up for what they believe in.... At the end of the film, a title card appears letting people know that they can download the song on iTunes, and the proceeds benefit the Chipotle Cultivate Foundation, which is dedicated to creating a sustainable, healthy, and equitable food future. People responded and the song reached number one on the iTunes Country chart. ([15])When interpreted through the lens of the reflexive strategy, Chipotle's ""bold"" move was to accept and amplify activist groups' criticisms of the fast-food industry's sourcing and production practices, rather than attempting to deny, rationalize, or obscure these problems through conventional images of consumers enjoying their fast-food meals. The campaign thereby draws an ideological distinction between Chipotle (as a reformist enterprise returning to more humane and sustainable agricultural practices) and the broader fast-food industry that is portrayed as having debased the time-honored practice of farming in the name of speed, efficiency, and cost reduction. In this way, Chipotle aligned itself not with the interests of the fast-food industry at large, but with activist groups who are seeking to reform the broader system of industrialized food production. Chipotle further reinforced the credibility of their reflexive strategy by investing additional resources to support the broader cause (i.e., creating a synergy between the reflexive and the perfective strategy). Humanistic rebel strategyThis strategy promotes the brand as a means for reconstituting meaningful social connections and breaking down societal boundaries that artificially separate people. To avoid being just another nostalgic marketing ode, this strategy should take a critical stance toward selected status quo consumption and marketing practices. The intended message is that the conscious capitalist brand is enabling consumers to resist or escape the dehumanizing and/or isolating influences of materialism, status consciousness, and upward-ratcheting lifestyle competitions.IKEA has run numerous campaigns that align with the humanistic rebel strategy. These campaigns embed its conscious capitalist commitments to sustainability and support of social justice issues, such as gender equity and LGBTQ rights, in a home-as-haven brand narrative. In these ads, the IKEA-furnished home represents a therapeutic space where people can, at least temporarily, unplug from the stresses and distractions of the ""networked life"" ([88], p. 17) and experience meaningful human connections and emotional fulfillment.More than just a haven, however, IKEA often portrays the home as an active force that keeps at bay the outside forces that would interfere with the pleasures of slow living. In an ad titled ""Home Is a Haven,"" we see a father and daughter running to their house during a rainstorm. As they approach the front door, the child's teddy bears spring to life as human-sized entities (whose muscular physiques resemble bouncers at a club). The bears rearrange the house into an open play area and protect the dad from intrusive calls and other outside distractions. We watch as father and daughter play dress-up and numerous other games, eventually falling asleep after their fully engaged bonding time (https://www.youtube.com/watch?v=wGgcYNlH02g).IKEA's ""Let's Relax"" commercial presents a pointedly critical take on the performative, competitive affectations of Instagram micro-influencers, for whom everyday social activities are treated as an instrumental means to garner likes and followers. In the ad, we first observe an eighteenth-century family about to begin formal dinner in a very well-appointed dining room. Suddenly, the father halts the proceedings so that an artist can paint a portrait of the meal, which is immediately transported across the town in a horse-drawn carriage so that affirmative thumbs up gestures from the populace can be tabulated. The scene then suddenly shifts to a modern-day kitchen table, where the same father meticulously photographs the family meal, while his wife and children begrudgingly wait for this documenting ritual to end. The dad sheepishly retires his camera, and the family begins their more enjoyable and authentic social interactions, all framed by the closing caption: ""Relax: It's a meal, not a competition"" (https://www.youtube.com/watch?v=2BXRGzjo1%5fQ).Drawing on our semiotic framework, we anticipate that conscious capitalist brands would gain the most authenticating benefit from the humanistic rebel strategy when they present their brands as ideological allies ([48]; [49]) of consumers who are sensitized to the psychological and social costs of careerism, exclusionary status hierarchies, and the calculated practices of social media self-presentations. In this way, the humanistic rebel strategy undercuts the elitist critique by suggesting that a conscious capitalist brand enables consumers to tap into more basic and rewarding emotional and sensory experiences. It further emphasizes that helping people, from all walks of life, feel genuinely connected to each other is an important and accessible way to make the world a better place. ConclusionDrawing from structural semiotics ([40]), we have developed a conceptual framework that can be used to analyze the cultural contradictions of authenticity, as they emerge in a given market context, and then to identify strategies for combatting their disauthenticating associations. Our analytic approach recognizes that perceptions of authenticity are constructed and contested in a dynamic cultural system. When negotiating such dynamism, marketing managers need to identify strategically significant patterns in the flux of cultural change and to adroitly react to cultural flash points, competitive shifts, and other exogenous shocks that could undermine the credibility of their existing authenticity claims. Whether undertaken in the context of conscious capitalist brands, status-marketing luxury goods, price-driven big-box retailers, or sharing-economy enterprises such as Uber or Airbnb, marketing managers can use our semiotic approach to more effectively negotiate the sociocultural complexity inherent to the process of authenticating their strategic assets. " 3,"Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The ""Word-of-Machine"" Effect"," Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel ""word-of-machine"" effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person's unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).","Recommendations driven by artificial intelligence (AI) are pervasive in today's marketplace. Ten years ago, Amazon introduced its innovative item-based collaborative filtering algorithm, which generates recommendations by scanning through a person's past purchased or rated items and pairing them to similar items. Since then, more and more companies are leveraging advances in AI, machine learning, and natural language processing capabilities to provide relevant and in-the-moment recommendations. For example, Netflix and Spotify use AI and deep learning to monitor a user's choices and provide recommendations of movies or music. Beauty brands such as Proven, Curology, and Function of Beauty use AI to make recommendations about skincare, haircare, and makeup. Real estate services such as OJO Labs, REX Real Estate, and Roof.ai have replaced human real estate agents with chatbots powered by AI. AI-driven recommendations are also pervading the public sector. For example, the New York City Department of Social Services uses AI to give citizens recommendations about disability benefits, food assistance, and health insurance.In response to the proliferation of AI-enabled recommendations and building on long-standing research on actuarial judgments ([12]; [17]; [30]), recent marketing research has focused on whether consumers will be receptive to algorithmic advice in various domains ([ 9]; [14]; [24]; [26]; [27]). However, no prior empirical investigation has systematically explored if hedonic/utilitarian trade-offs in decision making determine preference for, or resistance to, AI-based (vs. human-based) recommendations.We focus our investigation on hedonic/utilitarian attribute trade-offs because of their influence on both consumer choice and attitudes ([ 6]; [11]). Specifically, we examine when and why hedonic/utilitarian attribute trade-offs in decision making influence whether people prefer or resist AI recommenders. This question is of pivotal importance for managers operating in both the private and public sectors who are looking to harness the potential of AI-driven recommendations.Across nine studies and using a broad array of both attitudinal and behavioral measures, we provide evidence of a ""word-of-machine"" effect. We define ""word of machine"" as the phenomenon by which hedonic/utilitarian attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. We suggest that the word-of-machine effect stems from a lay belief about differential competence perceptions regarding AI and human recommenders. Specifically, we show that people believe AI recommenders are more competent than human recommenders to assess utilitarian attribute value and generate utilitarian-focused recommendations. By contrast, people believe that AI recommenders are less competent than human recommenders to assess hedonic attribute value and generate hedonic-focused recommendations. As a consequence, and as compared with human recommenders, individuals are more (less) likely to choose AI recommenders when utilitarian (hedonic) attributes are important or salient, such as when a utilitarian (hedonic) goal is activated.Our research is both theoretically novel and substantively impactful. A first set of theoretical contributions relates to research on the psychology of automation and on human–technology interaction ([12]; [17]; [30]). The pervasiveness of AI-driven recommendations has led to a burgeoning body of research examining whether consumers are receptive to the advice of algorithms, statistical models, and artificial intelligence ([14]; [24]; [27]). With respect to this literature, we make three novel contributions. First, we extend it by addressing the previously unexplored question of when and why hedonic/utilitarian trade-offs in decision making influence preference for or resistance to AI recommenders. Second, we show under what circumstances AI-driven recommendations are preferred to, and therefore more effective, than human ones: when utilitarian attributes are relatively more important or salient than hedonic ones. These results are especially noteworthy, as most research in this area has documented a robust and generalized resistance to algorithmic advice (for exceptions, see [ 9]; [15]; [26]). Third, we explore under what circumstances consumers will be amenable to AI recommenders in the context of human–AI partnerships: when AI supports rather than replaces a human. These results are also novel as researchers have just begun devising AI systems capable of deciding when to defer (vs. not defer) to a human ([18]), and empirical investigations are yet to examine if consumers will embrace such hybrid human–AI decision making.Our research makes a second theoretical contribution to the literature on hedonic and utilitarian consumption ([ 1]; [22]; [31]; [44]). Prior research in this area has examined how the evaluation of hedonic and utilitarian products depends on characteristics of the task, locus of choice, and justifiability of choice (e.g., [ 5]; [ 8]; [34]). However, research in this area has not addressed the question of whether shifts in hedonic/utilitarian trade-offs in decision making determine preference for the source of a recommendation (e.g., an AI vs. a human recommender). Recent developments of AI have brought this question to the fore, making it of critical importance for companies seeking to leverage the potential of AI-driven recommendations.From a managerial perspective, our results are useful for companies in both the private and public sectors that are looking to leverage AI recommenders to better reach their customers. As we investigate when consumers prefer AI over human recommenders, our findings are useful for companies debating if and how to effectively leverage AI-based recommendation systems. Our findings have implications for a host of marketing decisions. For instance, our results indicate that a shift away from hedonic attributes and toward utilitarian attributes leads to consumers preferring AI recommenders. Accordingly, AI recommenders may be more aligned with functional positioning strategies than experiential ones. In addition, emphasizing utilitarian benefits may be relatively more impactful with an AI-based system than emphasizing hedonic benefits. Taken together, our research and findings provide actionable insights for managers looking for ways to leverage AI to orchestrate consumer journeys so as to successfully move customers through the funnel, increase the likelihood of successful transactions, and, overall, optimize the customer experience at each phase of the journey. Theoretical Development Hedonic and Utilitarian ConsumptionAlthough consumption involves both hedonic and utilitarian considerations, consumers tend to view products as either predominantly hedonic or utilitarian (for a review, see [23]). Hedonic consumption is primarily affectively driven, based on sensory or experiential pleasure, reflects affective benefits, and is assessed on the basis of the degree to which a product is rewarding in itself ([ 8]; [11]; [21]). Utilitarian consumption is instead more cognitively driven, based on functional and instrumental goals, reflects functional benefits, and is assessed on the basis of the degree to which a product is a means to an end ([ 8]; [11]; [21]).Prior research on hedonic/utilitarian consumption has focused on the effect of characteristics of the task on product judgments. For instance, choice tasks tend to favor utilitarian options, whereas rating tasks tend to favor hedonic options ([ 5]; [34]), and forfeiture increases the relative salience of hedonic attributes compared to acquisition ([13]). Justifiability leads people to assign greater weight to utilitarian (vs. hedonic) options ([34]), and hedonic (vs. utilitarian) choices are associated with greater perceived personal causality ([ 8]).Although spanning over a decade, research on hedonic/utilitarian consumption has not yet addressed the question of whether hedonic and utilitarian trade-offs influence preference for the source of a recommendation (AI vs. human). This question has come to the fore given its importance for managers looking to leverage the potential of algorithmic recommendations. We discuss prior research on algorithmic recommendations in the next section. (Resistance to) Algorithmic RecommendationsEver since seminal work on statistical and actuarial predictive models was published ([12]; [17]; [30]), a large body of research has documented how statistical/actuarial models outperform clinical/human judgments in predicting a host of events, such as students' and employees' performance ([12]) and market demand ([37]). Despite the superior accuracy of algorithmic models, people tend to eschew them. With only a few exceptions ([ 9]; [15]; [26]), most of the extant literature has shown that people resist the advice of a statistical algorithm. For instance, recent research in the medical domain has shown that consumers may be more reluctant to utilize medical care delivered by AI providers than by comparable human providers ([27]; [28]). Corporate settings show similar patterns, with recruiters ([19]) and auditors ([ 7]) trusting their judgment and predictions more than algorithms.There are numerous reasons why people resist algorithmic recommendations. People (erroneously) believe that algorithms are unable to learn and improve ([12], [19]) and therefore lose confidence in algorithms when they see them err ([14]). People also believe that algorithms assume the world to be orderly, rigid, and stable and therefore cannot take into consideration uncertainty ([17]) and a person's uniqueness ([27]). Resistance to algorithmic advice may also be borne out of generalized concerns, such as people's fear of being reduced to ""mere numbers"" ([12]) and mistrust of algorithms' lack of empathy ([17]).We extend this literature and show circumstances under which people prefer (and not just resist) algorithmic recommendations. Specifically, we examine how and why hedonic/utilitarian trade-offs determine preference for, or resistance to, AI recommenders, as articulated in the next section. The Word-of-Machine Effect: Utilitarian/Hedonic Trade-offs Determine Preference for (or Resis...We hypothesize a word-of-machine effect, whereby hedonic and utilitarian trade-offs determine preference for or resistance to AI recommenders compared to human ones. We suggest that the word-of-machine effect stems from consumers' differing competence perceptions of AI and human recommenders in assessing attribute value and generating recommendations. Specifically, we suggest that people believe AI recommenders to be more (less) competent to assess utilitarian (hedonic) attribute value and generate utilitarian-focused (hedonic-focused) recommendations than human recommenders.These predictions rest on the assumption that people believe hedonic and utilitarian attribute value assessment to require different evaluation competences. Hedonic value assessment should map onto criteria on the basis of experiential, emotional, and sensory evaluative dimensions. By contrast, utilitarian value assessment should map onto criteria on the basis of factual, rational, and logical evaluative dimensions. This assumption is rooted in the very definition of hedonic and utilitarian value. Hedonic value is conceptualized as reflecting experiential affect associated with a product, sensory enjoyment, and emotions ([ 4]; [20]). Indeed, hedonic consumption tends to be affectively rich and emotionally driven ([ 8]). By contrast, utilitarian value is conceptualized as reflecting instrumentality, functionality, nonsensory attributes, and rationality ([ 4]; [20]). Overall, utilitarian consumption is cognitively driven ([ 8]).How do different types of recommenders (AI vs. human) then fare with respect to assessing hedonic and utilitarian attribute value? We suggest that people believe AI recommenders are more competent to assess utilitarian attribute value than human recommenders and less competent to assess hedonic attribute value than human recommenders. We attribute this lay belief to differing associations people have about how AI (vs. human) recommenders process and evaluate information. Lay beliefs are developed either directly through personal experience ([36]) or indirectly from the environment ([32]). Throughout childhood we learn firsthand that, as humans, we are able to perceive and connect with the outside world through our affective experiences. By contrast, we learn that AI, computers, and robots are rational and logical, and lack the ability to have affective, experiential interactions with the world. These associations are reflected in idioms such as ""thinking like a robot,"" which refers to thinking logically without taking into consideration more ""human"" aspects of a situation such as sensations and emotions. Thus, whereas AI and computers are associated with rationality and logic, humans are associated with emotions and experiential abilities. These associations are also echoed in books, songs, and movies. For example, in the Star Trek universe, the artificially intelligent form of life named Data has superior intellective abilities but is unable to experience emotions. Popular movies like Her, Ex Machina, and Terminator further reinforce these associations.Accordingly, we suggest that people believe AI recommenders are more competent than human recommenders when assessing information because they use criteria that rely relatively more on facts, rationality, logic, and, overall, cognitive evaluative dimensions. By contrast, we propose that people believe human recommenders are more competent than AI recommenders when assessing information because they use criteria that rely relatively more on sensory experience, emotions, intuition, and, overall, affective evaluative dimensions.Because people perceive AI and humans to have different competency levels when assessing information, and because assessment of utilitarian and hedonic attribute value underscore different evaluative foci, it follows that people perceive AI and humans to have different competency levels with respect to assessing utilitarian and hedonic attributes. This lay belief about competence perceptions forms the basis for the proposed word-of-machine effect. In summary, we predict that if utilitarian (hedonic) attributes are more important or salient, such as when a utilitarian (hedonic) goal is activated, people will be more (less) likely to choose AI recommenders than human recommenders.A final note warrants mention. As competence perceptions driving the word-of-machine effect are based on a lay belief, they are embedded in the cultural context. That is, humans are not necessarily less competent than AI at assessing and evaluating utilitarian attributes. Vice versa, AI is not necessarily less competent than humans at assessing and evaluating hedonic attributes. Indeed, AI selects flower arrangements for 1-800-Flowers and creates new flavors for food companies such as McCormick, Starbucks, and Coca-Cola ([41]). Overview of StudiesStudies 1a–b focus on product choice in field settings and show the main word-of-machine effect: that AI (human) recommenders lead to greater choice likelihood when a utilitarian (hedonic) goal is activated. Study 2 shows different perceptions that result from the two recommendation sources: AI (human) recommenders lead to higher evaluation of utilitarian (hedonic) attributes upon consumption. Study 3 shows that when a utilitarian (hedonic) attribute is considered important, consumers prefer AI (human) recommenders. Study 4 uses an analysis of mediation to corroborate the role of competence perceptions in explaining the word-of-machine effect while ruling out attribute complexity as alternative explanation. Studies 5–7 explore the scope of the word-of-machine effect by identifying boundary conditions. Study 5 shows that the effect is reversed for utilitarian goals when the recommendation needs to match to a person's unique preferences, a type of task people view AI as unfit to do. Study 6 shows that the effect is eliminated when AI is framed as ""augmented"" intelligence rather than artificial intelligence, that is, when AI enhances and supports a person rather than replacing them. Finally, Studies 7a–b test an intervention using the consider-the-opposite protocol to moderate the word-of-machine effect. Studies 1a–b: Preference for AI Recommenders When Utilitarian Goals Are ActivatedStudies 1a–b focus on the word-of-machine effect on actual product choice in field settings as a function of an activated utilitarian or hedonic goal. We first activated either a utilitarian or a hedonic goal and then, in an incentive-compatible setting, measured choice as a function of recommender. Study 1a: Hair Treatment Sample ProcedureTwo hundred passersby in a city in northeast United States participated in Study 1a on a voluntary basis. We handed willing passersby a leaflet explaining that we were conducting a blind test for products in the haircare industry and, specifically, for hair masks—a leave-in treatment for hair and scalp. Passersby read that for the purpose of the market test, we wanted them to select one of two hair mask samples solely on the basis of the instructions in the leaflet. These instructions activated, in a two-cell between-subjects design, either a hedonic or a utilitarian goal:[Hedonic] For the purpose of this blind test, it is very important that you set aside all thoughts you might already have about hair masks. Instead, we would like you to focus only on the following. Imagine that you have a ""hedonic"" goal. We would like you to imagine that the only things that you care about in a hair mask are hedonic characteristics, like how indulgent it is to use, its scent, and the spa-like vibe it gives you. When you make the next choice, imagine that there are no other things that are important for you in a hair mask.[Utilitarian] For the purpose of this blind test, it is very important that you set aside all thoughts you might already have about hair masks. Instead, we would like you to focus only on the following. Imagine that you have a ""utilitarian"" goal. We would like you to imagine that the only things that you care about in a hair mask are utilitarian characteristics, like how practical it is to use, its objective performance, and the chemical composition. When you make the next choice, imagine that there are no other things that are important for you in a hair mask.The leaflet further explained that there were two hair mask options from which they could choose. One option had been recommended by a person, and the other option had been recommended by an algorithm. The leaflet specified that the person and the algorithm had the same haircare expertise and that the pots of hair masks, available for pickup on a desk, all contained the same amount of fluid ounces. The pots were identical except for a marking of ""P"" if selected by a person or ""A"" if selected by an algorithm (stimuli in Web Appendix A). The key dependent variable was whether passersby chose the product selected by the person or by the algorithm. Results and discussionTo assess product choice, we compared the proportion of people who chose the product recommended by the algorithm with the proportion of people who chose the product recommended by the person depending on the activated goal (utilitarian vs. hedonic). The two proportions differed significantly (χ2( 1, N = 200) = 12.60, p =.001). As predicted, when a utilitarian goal was activated, more people chose the product recommended by the algorithm (67%) than by the person (33%; z = 4.81, p <.001). When a hedonic goal was activated, more people chose the product recommended by the person (58%) than by the algorithm (42%; z = 2.26, p =.024). Study 1b: Selection of House Properties ProcedureStudy 1b was a field study conducted over four consecutive days in Cortina, a resort town in northeast Italy. We selected this town because in 2026 it will host the Olympic games and is likely to experience a boom in its real estate market, which is the domain of the study. We secured the use of a centrally located bar and set up the study as follows. We placed an ad (translated to Italian) promoting a local real estate agency at the bar entrance. The ad headline reminded people of the opportunity to make fruitful real estate investments due to the upcoming Olympic games. In a two-cell, between-subjects design, we alternated the text in the ad to focus people on a hedonic or utilitarian goal:[Hedonic] With the Olympic games coming up, it is really important that you look for a real estate investment that is fun, enjoyable, and speaks to your emotions. You want a place that pleases your senses considering all the changes that will affect [name of town] in the next few years.[Utilitarian] With the Olympic games coming up, it is really important that you look for a real estate investment that is functional, useful, and speaks to your rationality. You want a place that is practical considering all the changes that will affect [name of town] in the next few years.At the bottom of the ad there were two envelopes described as containing a curated selection of available properties in Cortina that could fit with the opportunity in the ad (i.e., one of the activated goals). One property selection had been (ostensibly) curated by a person (the respective envelope read: ""one of [name of agency]'s agents has selected these properties"") and the other by an algorithm (the respective envelope read: ""[name of agency]'s proprietary algorithm has selected these properties""). The ad invited people to pick up only one of the two envelopes given the limited quantity of promotional materials (stimuli in Web Appendix B). The key dependent variable was whether people chose the selection made by the agent or by the algorithm. A waiter ensured that participants took only one of the two envelopes, and we excluded two participants who picked up two (final N = 229). Results and discussionWe compared the proportion of people who chose the selection made by the algorithm with the proportion of people who chose the selection made by the agent depending on the activated goal (utilitarian vs. hedonic). The two proportions differed significantly (χ2( 1, N = 229) = 29.33, p <.001). When the goal was utilitarian, more people chose the selection made by the algorithm (59.8%) than by the agent (40.2%; z = 3.07, p =.002), whereas when the goal was hedonic, more people chose the selection made by the agent (75.7%) than by the algorithm (24.3%; z = 7.52, p <.001).Together, Studies 1a–b show that when a utilitarian goal is activated, people are more likely to choose an AI recommender than a human recommender. When a hedonic goal is activated, people are less likely to choose an AI recommender than a human recommender. Study 2: AI Recommenders Shift Hedonic/Utilitarian Perceptions Upon ConsumptionStudy 2 examines the word-of-machine effect upon consumption. As conceptual information such as expectations affects food consumption experiences (e.g., [ 2]; [42]), we predicted that the type of recommender would affect perceptions of hedonic and utilitarian attributes upon actual consumption of a product (a chocolate cake). ProcedureOne hundred forty-four participants from a paid subject pool (open to students and nonstudents) at the University of Virginia completed this study (Mage = 27.5 years, SD = 9.5; 60.4% female). We told participants that we were testing chocolate cake recipes on behalf of a local bakery (stimuli in Web Appendix C). We told participants that the bakery had two options for chocolate cake recipes: one created using the ingredient selection of an AI chocolatier and one created using the ingredient selection of a human chocolatier. We specified that both the human and AI chocolatier had access to the same recipe database. We invited participants to look at the two chocolate cakes on top of a podium in a pop-up bakery/classroom desk. The two types of cake looked (and were) identical. We told participants that the two chocolate cakes, although based on different recipes, looked the same because the bakery did not want them to be influenced by the shape or the color. In a two-cell between-subjects design, we asked participants to consume either the chocolate cake whose recipe was selected by the human chocolatier or the one selected by the AI chocolatier. After consuming the cake, we measured hedonic/utilitarian attribute perceptions by asking participants to rate the cake on two hedonic items (indulgent taste and aromas; pleasantness to the senses [vision, touch, smell, etc.]) and two utilitarian items (beneficial chemical properties [antioxidants]; healthiness [micro/macro nutrients, etc.]) on seven-point scales anchored at 1 = ""very low"" and 7 = ""very high."" The order of hedonic and utilitarian items was randomized. Results and Discussion Hedonic attribute perceptionsA one-way analysis of variance (ANOVA) on the average of the two hedonic items (r =.87, p <.001) revealed that, upon consumption, participants rated the chocolate cake as having lower hedonic value when based on the recommendation of an AI chocolatier than a human one (MAI = 4.57, SD = 1.38; MH = 6.17, SD = 1.03; F( 1, 142) = 61.33, p <.001). Utilitarian attribute perceptionsA one-way ANOVA on the index of the two utilitarian items (r =.84, p <.001) revealed that, upon consumption, participants rated the chocolate cake as having higher utilitarian value when based on the recommendation of an AI chocolatier than a human one (MAI = 5.48, SD = 1.21; MH = 5.02, SD = 1.35; F( 1, 142) = 61.33, p =.034).Thus, Study 2 shows that the word-of-machine effect extends to actual consumption and that the type of recommender influences people's perceptions of hedonic/utilitarian trade-offs. AI recommenders led participants to perceive greater utilitarian attribute value and lower hedonic attribute value compared to human recommenders. Study 3: Preference for AI Recommenders When Utilitarian Attributes Are More ImportantStudy 3 further tests the word-of-machine effect. Instead of activating hedonic/utilitarian goals as in Studies 1a–b, we measured the importance given to hedonic/utilitarian attributes with respect to a specific product category (winter coats). Then, we assessed relative preference for a human or an AI recommender. We expected people to prefer AI to human recommenders when utilitarian attributes were more important to them, and to prefer human over AI recommenders when hedonic attributes were more important to them. We benchmarked these hypotheses with a condition in which people chose between two human recommenders, wherein we expected recommender preference to be uncorrelated with importance assigned to hedonic/utilitarian attributes. ProcedureThree hundred three respondents (Mage = 38.0 years, SD = 11.1; 49.5% female) recruited on Amazon Mechanical Turk participated in exchange for monetary compensation. Participants imagined that they were planning to purchase a new winter coat (as it was the winter season) and were looking for recommendations. Participants read that winter coats have functional/utilitarian aspects (""Winter coats have functional or utilitarian aspects, such as insulating power, breathability, and the degree to which the coat is rain and wind proof"") and sensory/hedonic aspects (""Winter coats have sensory or hedonic aspects, such as the color and other aesthetics, the way the fabric feels to the touch, and the degree to which the coat fits well""). Then, to measure the importance of hedonic/utilitarian attributes, participants rated the extent to which, in general, they cared about sensory/hedonic and functional/utilitarian aspects in winter coats (1 = ""mostly care about functional/utilitarian aspects,"" and 7 = ""mostly care about sensory/hedonic aspects"").Participants then read that to get recommendations about winter coats, they could rely on one of two shopping assistants, X or Y. We specified that both assistants had access to the same type and size of database, would charge the same fees, would generate recommendations autonomously, and were trained to serve users well and to the best of their capacity. To control for the possibility that different recommenders would be associated with different service quality perceptions, we also specified that the two shopping assistants had the same rating of 4.9/5.0 stars provided by 687 consumers that had used their services in the past. To manipulate choice set, half of the participants chose between two human shopping assistants (both X and Y were people and were described as two different sales associates at that particular retailer), and the other half chose between a human assistant, X, and an AI assistant, Y. Thus, whereas X was always human, Y was either human or AI depending on the condition. Finally, participants indicated their preference for one of the assistants (1 = ""definitely shopping assistant X,"" 4 = ""indifferent,"" and 7 = ""definitely shopping assistant Y""). Results and DiscussionWe regressed recommender preference on choice set (human–human vs. human–AI), hedonic/utilitarian attribute importance, and their interaction. This analysis revealed significant main effects of choice set (b =.85, t(299) = 5.49, p <.001) and hedonic/utilitarian attribute importance (b =.32, t(299) = 7.46, p <.001), as well as a significant two-way interaction (b = −.29, t(299) = −6.91, p <.001). As hedonic/utilitarian attribute importance was continuous, we explored the interaction using the Johnson–Neyman floodlight technique ([39]), which revealed a significant effect of recommender preference in human–AI choice set for levels of hedonic/utilitarian attribute importance lower than 2.35 (bJN =.15, SE =.08, p =.050) and higher than 3.36 (bJN = −.14, SE =.07, p =.050). That is, the more participants cared about utilitarian attributes (values lower than 2.35 on the seven-point scale), the more they preferred an AI assistant over a human one. Conversely, the more participants cared about hedonic attributes (values higher than 3.36 on the seven-point scale), the more they preferred a human assistant over an AI one. As predicted, in the human–human choice set, which served as the control condition, participants were indifferent between the two assistants (M = 3.98, SD =.34) and recommender preference was uncorrelated with hedonic/utilitarian attribute importance (r =.116, p =.162; see Figure 1).Graph: Figure 1. Results of Study 3: Preference for AI (human) recommenders when utilitarian (hedonic) attributes are more important.Notes: The y-axis represents preference for recommender measured on a seven-point scale anchored at 1 = ""definitely shopping assistant X,"" and 7 = ""definitely shopping assistant Y."" The x-axis represents importance of hedonic/utilitarian attributes, measured on a seven-point scale anchored at 1 = ""mostly care about functional/utilitarian aspects,"" and 7 = ""mostly care about sensory/hedonic aspects."" The shaded region represents area of significance.These results provided correlational evidence that hedonic/utilitarian attribute importance predicts preference between human and AI recommenders. The next study utilizes an analysis of mediation to test competence perceptions as drivers of the word-of-machine effect. Study 4: Mediation by Competence Perceptions: Ruling Out ComplexityStudy 4 uses an analysis of mediation to measure competence perceptions as lay beliefs underlying the word-of-machine effect. In addition, this study tests attribute complexity as an alternative explanation: a belief that AI recommenders are better capable to process more complex attribute information than human recommenders. One could argue that utilitarian attributes seem more complex to evaluate than hedonic attributes. If this argument is accurate, preference for AI recommenders when utilitarian attributes are more salient could be explained by a lay belief about the recommender's ability, higher for AI recommenders, to deal with complexity.[ 7] We tested this alternative explanation by manipulating attribute complexity orthogonally to recommender type (human, AI) and activated goal (hedonic, utilitarian). We manipulated attribute complexity by way of number of product attributes, which is consistent with prior research ([25]; [40]). ProcedureFour hundred two participants (Mage = 38.5 years, SD = 12.6; 46% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a 2 (complexity: low, high) × 2 (goal: hedonic, utilitarian) × 2 (recommender: human, AI) between-subjects design.Participants read about the beta testing of a new app created to give recommendations of chocolate varieties by relying on one of two sources: a human or an AI master chocolatier (i.e., a computer algorithm). We told participants that the human and AI recommenders relied on the same database of chocolate varieties and operated autonomously. The app had the same cost regardless of recommender. Participants saw screenshots of the app (Figure 2).Graph: Figure 2. Stimuli of Study 4.We specified that the ratings of the chocolate varieties in the data set were not based on personal experience but rather that they had been rated by consumers and manufacturers in terms of certain dimensions that varied by complexity condition. In the high complexity condition, we described the chocolate varieties as being rated on eight attributes, four of which were hedonic (sensory pleasure, taste, fun factor, and pairing combinations) and four of which were utilitarian (chemical profile, nutritional index, digestibility profile, and health factor). In the low complexity condition, we described the chocolate varieties as being rated on two attributes, one of which was hedonic (sensory pleasure) and one of which was utilitarian (chemical profile).We then activated either a hedonic or a utilitarian goal by asking participants to set aside all thoughts they might already have had about chocolate and instead imagine that they wanted a recommendation based only on ( 1) sensory pleasure, taste, fun factor, and pairing combinations (hedonic/high complexity); ( 2) sensory pleasure (hedonic/low complexity); ( 3) chemical profile, nutritional index, digestibility profile, and health factor (utilitarian/high complexity); or ( 4) chemical profile (utilitarian/low complexity). Finally, we manipulated recommender in a two-cell (recommender: human, AI) between-subjects design by telling participants that in the version of the app they were considering, it was either the human or the AI master chocolatier that would give them a recommendation.As a behavioral dependent variable, we asked participants if they wanted to download the chocolate recommendation at the end of the survey (yes, no), specifying that payment would not be conditional on electing to download the recommendation (which is consistent with previous research; see [10]). We then measured the hypothesized mediator (competence perceptions) by asking participants to rate the extent to which they thought the human (AI) recommender ( 1) was competent to recommend the type of chocolate they were looking for and ( 2) could do a good job recommending the type of chocolate they were looking for (1 = ""strongly disagree,"" and 7 = ""strongly agree""; r =.89, p <.001).[ 8] At the very end of the survey, participants who elected to download the recommendation were automatically directed to a downloadable PDF document with information about the chocolate (a relatively more indulgent hazelnut-based chocolate called ""gianduiotti"" in the hedonic condition or a relatively healthier chocolate toasted at low temperature called ""crudista"" in the utilitarian condition). Results and Discussion BehaviorWe assessed behavior (i.e., the proportion of participants who decided to download vs. not download the recommendation) by using a logistic regression with complexity, goal, recommender, and their two-way and three-way interactions as independent variables (all contrast coded) and download (1 = yes, 0 = no) as dependent variable. We found no significant main effect of complexity (B = −.04, Wald =.09, 1 d.f., p =.77) or goal (B =.03, Wald =.06, 1 d.f., p =.81), and we found a marginally significant main effect of recommender (B =.25, Wald = 3.75, 1 d.f., p =.053). The three-way goal × recommender × complexity interaction was not significant (B = −.11, Wald =.80, 1 d.f., p =.37), ruling out the role of complexity. In terms of two-way interactions, complexity did not interact with goal (B = −.13, Wald = 1.04, 1 d.f., p =.31) nor with recommender (B = −.18, Wald = 1.99, 1 d.f., p =.16). Replicating previous results, the two-way goal × recommender interaction was significant (B =.75, Wald = 34.60, 1 d.f., p <.001). The AI recommender led to more downloads than the human recommender when the goal was utilitarian (MAI = 82%, MH = 63%; z = 3.10, p =.002) and fewer downloads when the goal was hedonic (MAI = 52%, MH = 88%; z = −5.63, p <.001). Competence perceptionsA 2 × 2 × 2 ANOVA on competence perceptions revealed no significant main effect of complexity (F( 1, 394) = 1.24, p =.27) and significant main effects of goal (F( 1, 394) = 8.99, p =.003) and recommender (F( 1, 394) = 19.81, p <.001). The three-way complexity × goal × recommender interaction was not significant (F( 1, 394) =.64, p =.44), ruling out complexity. In terms of two-way interactions, complexity did not interact with goal (F( 1, 394) =.61, p =.44), nor with recommender (F( 1, 394) =.36, p =.55). Importantly, the two-way goal × recommender interaction was significant (F( 1, 394) = 57.63, p <.001). Planned contrasts revealed that participants perceived the AI recommender as more competent than the human recommender in the case of a utilitarian goal (MAI = 5.92, SDAI = 1.10; MH = 5.50, SDH = 1.38; F( 1, 394) = 4.90, p =.027) and less competent in the case of a hedonic goal (MAI = 4.51, SDAI = 1.77; MH = 6.13, SDH =.96; F( 1, 394) = 73.04, p <.001). Moderated mediationWe ran a moderated mediation model using PROCESS Model 8 ( 5,000 resamples; Hayes 2018). In this model, the moderating effect of goal takes place before the mediator (competence perceptions). The interaction between recommender and goal was significant (95% CI =.38 to.64) in the path between the independent variable and the mediator but not in the path between the independent variable and the dependent variable (95% CI = −.08 to.63). As predicted, the indirect effect recommender → competence perceptions → download was significant but in the opposite direction conditionally on the moderator (hedonic: 95% CI = 1.19 to 2.40; utilitarian: 95% CI = −.88 to −.06).These results provide evidence for the hypothesized role of competence perceptions as drivers of the word-of-machine effect. Participants rated AI recommenders as more (less) competent in the case of utilitarian (hedonic) goals. Differential competence perceptions explained higher choice likelihood for the AI's recommendation than the human's if a utilitarian goal had been activated and lower choice likelihood for the AI's recommendation than the human's if a hedonic goal had been activated. Furthermore, we did not find evidence that the word-of-machine effect was moderated by complexity. The next three studies tested the scope of the word-of-machine effect by identifying boundary conditions. Study 5: Testing Unique Preference Matching as a Boundary ConditionStudy 5 explores a circumstance under which the word-of-machine effect might reverse: when consumers want a recommendation that matches their unique needs and preferences.[ 9] Matching a recommendation to one's preferences is valued and might even be expected ([16]). In this study, we tested the hypothesis that consumers view the task of matching a recommendation to one's unique preferences as being better performed by a person than by AI.[10] This argument is in line with recent research in the medical domain showing that consumers perceive AI as less able than a human physician to tailor a medical recommendation to their unique characteristics and circumstances ([27]). Thus, we expected people to choose AI recommenders at a lower rate and, conversely, choose human recommenders at a higher rate if matching to unique preferences was salient, even in the case of an activated utilitarian goal. In other words, if matching to unique preferences was salient, we expected people to prefer a human recommender for both hedonic and utilitarian goals. We tested this possibility by manipulating whether participants' desire to have a recommendation matched to their unique needs and preferences was salient and then measuring their choice of recommender. ProcedureFive hundred forty-five respondents (Mage = 39.0 years, SD = 12.9; 46.6% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a 2 (goal: hedonic, utilitarian) x 2 (matching: unique preferences, control) between-subjects design. Participants read information about the beta testing of a new smartphone app offered by a real estate service. The app would allow users to chat with a Realtor to find properties to buy or rent. Participants further read that there were two versions of this app. In one version of the app, users would interact with a human Realtor, and in the other version, users would interact with an AI Realtor (i.e., a computer algorithm). Participants saw screenshots of the app (Figure 3) and read about how the app would work: the users would indicate what attributes they were looking for in a property (square footage, number of rooms, budget) and the [Realtor/AI Realtor] would use [their/its] training and knowledge to make apartment recommendations. We specified that both the human and AI Realtors had access to the same number and type of property listings. We then activated either a hedonic or a utilitarian goal by asking participants to set aside all thoughts they might already have had about apartments and instead imagine that they wanted a recommendation based only on: ( 1) how trendy the neighborhood is, the apartment views, aesthetics (hedonic goal condition) or ( 2) distance to their workplace, proximity to public transport, functionality (utilitarian goal condition; based on [ 6]). Finally, to make unique preference matching salient, we told half of the participants that it was very important for them to get a recommendation that would be matched to their unique needs and personal preferences. Participants in the control condition were not focused on unique preference matching. As a dependent variable, we measured choice of recommender by asking participants if, given the circumstances described, they wanted to chat with the human or the AI Realtor.Graph: Figure 3. Stimuli (top) and results (bottom) of Study 5: The word-of-machine effect is reversed for utilitarian goals if the recommendation needed to match participants' unique preferences.Notes: The y-axis represents the proportion of participants who chose to chat with the human versus AI realtor. Results and DiscussionWe assessed choice on the basis of the proportion of participants who decided to chat with the human versus AI Realtor by using a logistic regression with goal, matching, and their two-way interaction as independent variables (all contrast coded) and choice (0 = human, 1 = AI) as a dependent variable. We found significant effects of goal (B = 1.75, Wald = 95.70, 1 d.f., p <.000) and matching (B =.54, Wald = 24.30, 1 d.f., p <.000). More importantly, goal interacted with matching (B =.25, Wald = 5.33, 1 d.f., p =.021). Results in the control condition (when unique preference matching was not salient) replicated prior results: in the case of an activated utilitarian goal, a greater proportion of participants chose the AI Realtor (76.8%) over the human Realtor (23.2%; z = 8.91, p <.001), and when a hedonic goal was activated, a lower proportion of participants chose the AI (18.8%) over the human Realtor (81.2%; z = 10.35, p <.001). However, making unique preference matching salient reversed the word-of-machine effect in the case of an activated utilitarian goal: choice of the AI Realtor decreased to 40.3% (from 76.8% in the control; z = 6.17, p <.001). That is, making unique preference matching salient turned preference for the AI Realtor into resistance despite the activated utilitarian goal, with most participants choosing the human over the AI Realtor. In the case of an activated hedonic goal, making unique preference matching salient further strengthened participants' choice of the human Realtor, which increased to 88.5% from 81.2% in the control, although the effect was marginal, possibly due to a ceiling effect (z = 1.66, p =.097).Overall, whereas the word-of-machine effect replicated in the control condition when unique preference matching was salient, participants preferred the human Realtor over the AI recommender both in the hedonic goal conditions (human = 88.5%, AI = 11.5%; z = 12.40, p <.001) and in the utilitarian goal conditions (human = 59.7%, AI = 40.3%; z = 3.24, p =.001; Figure 3), corroborating the notion that people view AI as unfit to perform the task of matching a recommendation to one's unique preferences.These results show that preference matching is a boundary condition of the word-of-machine effect, which reversed in the case of a utilitarian goal when people had a salient goal to get recommendations matched to their unique preferences and needs. The next study tests another boundary condition. Study 6: Testing Augmented Intelligence as a Boundary ConditionStudy 6 explores under what circumstances the word-of-machine effect is eliminated, and it tests the role of AI as boundary condition. Studies 1–5 tested cases in which the role of AI was to replace human recommenders. Study 6 explores the case in which AI is leveraged to assist and augment human intelligence. ""Augmented intelligence"" involves AI's assistive role in enhancing and amplifying human intelligence instead of replacing it ([ 3]). So far, we have showed that consumers resist AI recommenders when a hedonic goal is activated. In Study 6, we tested the hypothesis that consumers will be more receptive to AI recommenders, even in the case of a hedonic goal, if the AI recommender assists and amplifies a human recommender who retains the role of ultimate decision maker. In this case, we expected people to believe that the human decision maker would compensate for the AI's relative perceived incompetence in the hedonic realm. We expected the reverse effect in the case of a utilitarian goal. In other words, we expected that augmented intelligence—a human–AI hybrid decision making model— would help bolster AI to the level of humans for hedonic decision making and help bolster humans to the level of AI for utilitarian decision making. In addition, we added a control condition in Study 6 in which neither recommender was mentioned to serve as a baseline measure of participants' perceptions of hedonic and utilitarian attributes. ProcedureFour hundred four respondents (Mage = 40.2 years, SD = 12.5; 48.9% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a three-cell (recommender: human, artificial intelligence, augmented intelligence) between-subjects design. A fourth control condition contained no recommender manipulation and served as the baseline.The stimuli and procedure were identical to those of Study 4. Participants read about the beta testing of a new app created to give recommendations of chocolate varieties by relying on one of two sources: a human or an AI master chocolatier. Participants read that human and AI recommenders relied on the same database, which comprised a large number of chocolate varieties that had been rated by consumers and manufacturers. Participants read that the app had the same cost regardless of the type of recommender it relied on. Finally, participants read that the app would suggest a curated selection of five chocolate bars.We then manipulated recommender by randomly assigning participants to ( 1) a human condition, in which a human chocolatier would curate the chocolate section; ( 2) an artificial intelligence condition, in which an AI chocolatier (i.e., a computer algorithm) would curate the chocolate section; or ( 3) an augmented intelligence condition, in which the AI chocolatier would assist the human chocolatier in the curation of the chocolate selection. Specifically, participants read:[Human condition] In the version of the app we are testing today, it is the human chocolatier that curates a selection of chocolate bars. This selection contains five chocolate bars selected by the human chocolatier. That is, it is a person who selects chocolate bars. This version of the app is technically called ""human intelligence,"" because it uses what human intelligence can do.[Artificial intelligence condition] In the version of the app we are testing today, it is the A.I. chocolatier that curates a selection of chocolate bars. This selection contains five chocolate bars selected by the A.I. chocolatier. That is, it is a computer algorithm that selects chocolate bars. This version of the app is technically called ""artificial intelligence,"" because it uses a computer algorithm to substitute and replace what human intelligence can do.[Augmented intelligence condition] In the version of the app we are testing today, it is the A.I. chocolatier that curates a selection of chocolate bars. This selection contains five chocolate bars selected by the A.I. chocolatier. That is, it is a computer algorithm that selects chocolate bars. The computer algorithm makes the initial selection and assists a human chocolatier, who will make the final decision about which chocolate bars to recommend. This version of the app is technically called ""augmented intelligence,"" because it uses a computer algorithm to enhance and augment what human intelligence can do.The control condition entailed no recommender manipulation; instead, it merely included a description of the app and no information about the source of the chocolate bar recommendation. As a dependent variable, we measured hedonic attribute perceptions with two items (indulgent taste and aromas; pleasantness to the senses [vision, touch, smell, etc.]) and utilitarian attribute perceptions with two items (beneficial chemical properties [antioxidants, etc.]; healthiness [micro/macro nutrients, etc.]), all on seven-point scales anchored at 1 = ""very low,"" and 7 = ""very high."" The order of items was randomized. Results and Discussion Hedonic attribute perceptionsThe one-way ANOVA on the average of the two items measuring hedonic attribute perceptions (r =.79, p <.001) was significant (F( 1, 436) = 48.92, p <.001). In line with previous results, and replicating the word-of-machine effect, participants reported higher hedonic attribute perceptions when the recommender was human (MH = 6.00; SD = 1.06) than when the recommender was AI (Martificial_intelligence = 4.15, SD = 1.64; F( 1, 436) = 125.55, p <.001). However, when the AI recommender was augmenting human intelligence, the word-of-machine effect was eliminated: participants reported the same hedonic perceptions (Maugmented_intelligence = 5.74, SD = 1.11) as they did when the recommender was human (F( 1, 436) = 2.31, p =.129) and higher hedonic perceptions than when the recommender was AI alone (F( 1, 436) = 84.73, p <.001). Participants in the control condition reported lower hedonic perceptions (Mcontrol = 5.62, SD = 1.09) than participants in the human condition (F( 1, 436) = 5.32, p =.022) and higher hedonic perceptions than participants in the AI condition (F( 1, 436) = 77.92, p <.001). Control condition and augmented intelligence condition did not differ (F( 1, 436) < 1, p =.49). Utilitarian attribute perceptionsThe one-way ANOVA on the average of the two items measuring utilitarian attribute perceptions (r =.75, p <.001) was significant (F( 1, 436) = 6.60, p <.001). In line with previous results, and replicating the word-of-machine effect, participants reported higher utilitarian attribute perceptions when the recommender was AI (Martificial_intelligence = 5.24; SD = 1.41) than when the recommender was human (MH = 4.75, SD = 1.57; F( 1, 436) = 6.40, p =.012). However, when the AI recommender was augmenting human intelligence, the word-of-machine effect was eliminated: participants reported the same utilitarian perceptions (Maugmented_intelligence = 5.44, SD = 1.32) as they did when the recommender was AI alone (F( 1, 436) =.99, p =.321) and higher utilitarian perceptions than when the recommender was human (F( 1, 436) = 11.87, p <.001). Participants in the control condition reported the same utilitarian perceptions (Mcontrol = 4.70, SD = 1.56) as participants in the human condition (F( 1, 436) =.05, p =.820) and lower utilitarian perceptions than participants in both the AI (F( 1, 436) = 7.47, p =.007) and augmented intelligence conditions (F( 1, 436) = 13.22, p <.001; Figure 4).Graph: Figure 4. Results of Study 6: The word-of-machine effect is eliminated in the case of augmented intelligence (human–AI hybrid decision making).Notes: The y-axis represents hedonic attribute perceptions and utilitarian attribute perceptions measured on seven-point scales anchored at 1 = ""very low,"" and 7 = ""very high."" Error bars represent standard errors. The solid-line pairwise comparisons represent the word-of-machine effect. The dashed-line pairwise comparisons represent moderation by augmented intelligence: A human–AI hybrid decision making model bolsters AI to the level of humans for hedonic decision making, and humans to the level of AI for utilitarian decision making. Details of all pairwise comparisons are reported subsequently.Hedonic Attribute PerceptionsWord-of-machine effect: Human versus AI: F( 1, 436) = 125.55, p =.000Moderation by augmented intelligence (H + AI hybrid decision making bolsters AI to the level of humans for hedonic decision making): Human versus H + AI: F( 1, 436) = 2.31, p =.129AI versus H + AI: F( 1, 436) = 84.73, p =.000Control versus H: F( 1, 436) = 5.32, p =.022Control versus AI: F( 1, 436) = 77.92, p =.000Control versus H + AI: F( 1, 436) =.49, p =.486Utilitarian Attribute PerceptionsWord-of-machine effect: Human versus AI: F( 1, 436) = 6.40, p =.012Moderation by augmented intelligence (H + AI hybrid decision making bolsters H to the level of AI for utilitarian decision making): AI versus H + AI: F( 1, 436) =.99, p =.321H versus H + AI: F( 1, 436) = 11.87, p =.001Control versus H: F( 1, 436) =.05, p =.820Control versus AI: F( 1, 436) = 7.47, p =.007Control versus H + AI: F( 1, 436) = 13.22, p =.000These results delineate the scope of the word-of-machine effect and show a circumstance under which the effect is eliminated. Even when a hedonic goal was activated, AI recommenders fared as well as human recommenders as long as they were in a hybrid decision-making model in partnership with a human. Studies 7a–7b: Attenuating the Lay Belief Underlying the Word-of-Machine EffectStudies 7a and 7b test an intervention to attenuate the lay belief underlying the word-of-machine effect—that AI recommenders are less (more) competent than human recommenders in assessing hedonic (utilitarian) value. We used a protocol called ""consider-the-opposite,"" in which people are prompted to consider the opposite of what they initially believe to be true and take into account evidence that is inconsistent with one's initial beliefs. This protocol has been effectively used to correct biased beliefs in judgment, such as the explanation bias ([29]), confirmatory hypothesis testing ([43]), anchoring ([33]) and halo effects in marketing claims ([35]). Study 7a tests this intervention following the original protocol (i.e., [33]), and Study 7b tests a protocol that is relatively easier to implement and scale by embedding the intervention in a real chatbot. Study 7a: Testing the Original Consider-the-Opposite Protocol ProcedureThree hundred sixty-eight respondents (Mage = 39.8 years, SD = 12.5; 49.2% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a 2 (recommender: human, AI) × 2 (intervention: consider the opposite, control) between-subjects design.The stimuli and procedure were identical to those of Studies 4 and 6: participants read about a new app created to give chocolate recommendations by relying on either a human or an AI master chocolatier. We manipulated recommender between subjects by telling participants that, in the version of the app they were considering, it was either the human or the AI chocolatier that would suggest a curated selection of five chocolate bars. We also implemented the intervention between subjects by prompting half of the participants to ""consider the opposite"": consider the ways in which they could be wrong about what they expected the [human/AI] recommender to be good at (based on [33]):Think for a moment about what you expect the [human/AI] chocolatier to be good at when selecting chocolate bars. Before you rate the chocolate selection, we would like you to consider the opposite. Can your expectations about what the human chocolatier is good at when selecting chocolates be wrong? Imagine that you were trying to be as unbiased as possible in evaluating this chocolate selection—consider yourself to be in the same role as a judge or juror. Could the [human/AI] chocolatier be good at the opposite of what you expect them to be good at? Please write down some ways in which you could be wrong in terms of your expectations about what the [human/AI] chocolatier is good at when selecting chocolates.This prompt was absent for participants in the control condition. As a dependent variable, participants reported their perceptions of hedonic/utilitarian attributes of the curated selection of chocolate bars, measured on a seven-point scale ranging from 1 = ""sensory pleasure (taste, aromas, etc.)"" to 7 = ""healthy chemical properties (antioxidants, micro/macro nutrients, etc.)."" Thus, lower numbers indicated higher hedonic value. Results and discussionA 2 × 2 ANOVA on hedonic/utilitarian attribute perceptions revealed no significant main effect of intervention (F( 1, 364) =.25, p =.62), a significant main effect of recommender (F( 1, 364) = 65.17, p <.001), and a significant two-way recommender × intervention interaction (F( 1, 364) = 12.11, p =.001). Planned contrasts revealed that the word-of-machine effect replicated both in the control and intervention conditions, with lower hedonic perceptions (or, conversely, higher utilitarian perceptions) for AI recommenders than human recommenders (control conditions: MAI_control = 4.51, SD = 1.84, MH_control = 2.49, SD = 1.48; F( 1, 364) = 81.48, p <.001; intervention conditions: MAI_intervention = 3.99, SD = 1.78, MH_intervention = 3.18, SD = 1.44; F( 1, 364) = 8.93, p =.003; higher numbers indicate higher utilitarian/lower hedonic perceptions). More importantly, the intervention attenuated the word-of-machine effect and led to participants perceiving the AI's recommendation as having higher hedonic value compared with the control condition (MAI_intervention = 3.99, SD = 1.78, MAI_control = 4.51, SD = 1.84; F( 1, 364) = 7.66, p =.006) and the human recommendation as having higher utilitarian value compared to the control condition (MH_intervention = 3.18, SD = 1.44; MH_control = 2.49, SD = 1.48, F( 1, 364) = 4.59, p =.033; higher numbers indicate higher utilitarian/lower hedonic perceptions; Figure 5).Graph: Figure 5. Results of Study 7a: Prompting people to consider the opposite attenuated the word-of-machine effect.Notes: The y-axis represents perceived hedonic/utilitarian attribute value measured on a seven-point scale anchored at 1 = ""sensory pleasure (taste, aromas, etc.), and 7 = ""healthy chemical properties (antioxidants, micro/macro nutrients, etc.)""; therefore, higher numbers indicate higher utilitarian value/lower hedonic value. Error bars represent standard errors.Thus, these results provide evidence for a potential intervention that alleviates initial beliefs about, and therefore resistance to, AI recommenders: prompting people to consider the opposite. Study 7b: Testing a Consider-the-Opposite Intervention That Is Easier to Implement and ScaleStudy 7b builds on the original consider-the-opposite protocol and the results of Study 7a to test an intervention better suited for implementation and scalability in a real-world setting. To do so, we created a real chatbot that participants could interact with and that delivered the intervention. ProcedureTwo hundred nighty-nine respondents (Mage = 40.4 years, SD = 12.6; 43.1% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a two-cell (intervention: consider the opposite, control) between-subjects design. Participants read about an app called ""Cucina"" that would rely on AI to give recipe recommendations. The app worked by giving users the chance to chat with the AI Chef and ask for recipe suggestions and recommendations. Participants further read that they could try out the AI Chef by chatting with it in a web browser window. We created a chatbot ad hoc for this experiment by embedding a JavaScript in the Qualtrics survey (Figure 6). The chatbot was programmed to first introduce itself: ""Hello I am an A.I. Chef at Cucina! Thank you for trying out our app! What is your name?"" Participants could then reply to the chatbot using a text box. We programmed the chatbot's next response to differ depending on the intervention condition:[Intervention: consider the opposite] ""Hi [participant's name]! I am here to suggest a recipe for you to try! Some people might think that an Artificial Intelligence Chef is not competent to give food suggestions...but this is a misjudgment. For a moment, set aside your expectations about me. When it comes to making food suggestions, could you consider the idea that I could be good at things you do not expect me to be good at? Okay, let's chat about food. How can I help you?""Graph: Figure 6.Stimuli of Study 7b.[Intervention: control] ""Hi [participant's name]! I am here to suggest a recipe for you to try! Okay, let's chat about food. How can I help you?""As a dependent variable, we measured hedonic/utilitarian attribute perceptions of the recipes suggested by the AI chatbot, as measured on a seven-point scale ranging from 1 = ""mostly based on sensory pleasure (taste, aromas, etc.)"" to 7 = ""mostly based on healthy chemical properties (antioxidants, micro/macro nutrients, etc.)."" Results and discussionA one-way ANOVA on hedonic/utilitarian attribute perceptions revealed that the intervention attenuated the word-of-machine effect and led to higher hedonic perceptions compared to the control condition (Mintervention = 3.75, SD = 1.46, Mcontrol = 4.25, SD = 1.37; F( 1, 297) = 9.15, p =.003; lower numbers indicate higher hedonic perceptions). These results corroborate those of Study 7a and provide evidence for a practical and relatively easier-to-implement intervention for managers looking to attenuate the lay belief underlying the word-of-machine effect. General DiscussionAs companies in the private and public sectors assess how to harness the potential of AI-driven recommendations, the question of how trade-offs in decision making influence preference for AI recommenders is of great importance. We address this question across nine studies and show a word-of-machine effect: the phenomenon by which hedonic and utilitarian trade-offs determine preference for (or resistance to) AI-driven recommendations. Studies 1a–1b show that a utilitarian (hedonic) goal makes people more (less) likely to choose AI recommenders than human ones. Study 2 shows that AI (human) recommenders lead to higher perceptions of utilitarian (hedonic) attributes upon consumption. Study 3 shows that people prefer AI (human) recommenders when utilitarian (hedonic) attributes are more important. Study 4 shows that differing competence perceptions underlie the word-of-machine effect and rule out complexity. Studies 5 and 6 identify boundary conditions: Study 5 shows that the word-of-machine effect is reversed for utilitarian goals if the recommendation needs to match a person's unique preferences, and Study 6 shows that the effect is eliminated when AI is framed as ""augmented"" rather than ""artificial"" intelligence, that is, in human–AI hybrid decision making. Finally, Studies 7a–7b tested an intervention to attenuate the word-of-machine effect. Theoretical ContributionsOur research makes several important theoretical contributions. A first set of contributions speaks to research on the psychology of automation and on human–technology interactions ([12]; [17]; [30]). First, we extend this literature by addressing the question of whether hedonic/utilitarian trade-offs in decision making drive preference for or resistance to AI recommenders. This question is novel, as prior research has not relied on differences inherent to hedonic/utilitarian consumption to predict people's reactions to receiving advice from automated systems.Second, we show under what circumstances AI-driven recommendations are preferred to, and therefore more effective, than human ones: when utilitarian attributes are relatively more important or salient than hedonic ones. Research in this area has largely focused on consumers' resistance to automated systems. For example, in the domain of performance forecasts, people are less likely to rely on the input of an algorithm than a person to make predictions about student performance, an effect that is due to the belief that algorithms, unlike people, cannot learn from their mistakes ([14]). In the domain of health care utilization, people are less likely to rely on an automated medical provider if a human provider is available, even when the two providers have the same accuracy ([27], [28]).Limited research has identified under what circumstances resistance to algorithmic advice is attenuated: if people have the opportunity to modify algorithms and thus exert control over them ([15]), if the human likeness of algorithms is increased ([ 9]), if the task entails a numeric estimate of a target ([26]), and if the algorithm is described as tailoring a recommendation to a person's unique case ([27], [28]). We extend this literature by showing circumstances in which consumers' resistance to AI may be reversed and by showing cases in which consumers even prefer automated systems: when they assign greater importance to utilitarian attributes or when a utilitarian goal is activated.Third, we explore under what circumstances consumers will be amenable to AI recommenders in the context of human–AI partnerships. We show that augmented intelligence helps bolster AI to the level of humans for hedonic decision making and helps bolster humans to the level of AI for utilitarian decision making. This contribution is important because it represents the first empirical test of augmented intelligence as an alternative conceptualization of artificial intelligence that focuses on AI's assistive role in advancing human capabilities. We hope that this contribution will prioritize new research focused on understanding the potential of AI in conjunction with humans rather than in contraposition, as this seems to be the advocated way forward by many practitioners ([ 3]; [18]).We also contribute to the literature on hedonic and utilitarian consumption ([ 1]; [22]; [31]; [44]). Literature in this area has identified the factors that influence evaluation of hedonic and utilitarian product dimensions. We extend this literature by investigating how hedonic/utilitarian attribute trade-offs influence the effectiveness of a source of a product recommendation (i.e., a human vs. an AI recommender; Studies 1a, 1b, 3–5) and how the source of a product recommendation influences hedonic/utilitarian perceptions (Studies 2, 6–7b). Managerial ImplicationsThe current speed of development and adoption of AI, machine learning, and natural language processing algorithms challenge managers to harness these transformative technologies to optimize the customer experience. Our findings are insightful for managers as they navigate the remarkable technology-enabled opportunities that are growing in today's marketplace. These new technologies are also experiencing a renewed prominence in public discourse. For instance, the U.S. government has established the National Artificial Intelligence Research and Development Strategy to address economic and social implications of AI.Our findings provide useful insights for both companies and public policy organizations debating if and how to effectively automate their recommendations systems. A company like Sephora relies both on human-based recommendations from sales associates and its customer base and AI-based recommendations through its Visual Artist app, a conversational bot that interacts with prospective shoppers. Our results suggest cases in which AI-based recommendations would be more effective (i.e., when utilitarian attributes are more salient or important, such as grooming products) and when they would be less effective (i.e., when hedonic attributes are more salient or important, such as fragrances).Our results are insightful for strategic and tactical marketing decisions. Marketers could prioritize functional positioning strategies over experiential ones in the case of AI-based recommendations for target segments for whom utilitarian attributes are more important. For instance, a company in the hospitality industry such as TripAdvisor should emphasize AI-based recommendations for business travel services and deemphasize AI-based recommendations for leisure travel services. Our results also apply to a host of tactical decisions such as marketing communications. Managers could communicate to their customers in a way that is aligned with a target segment's goal (i.e., hedonic vs. utilitarian) and emphasize the most effective points of parity/difference with competing brands or across different products in the portfolio. Companies like Netflix and YouTube could emphasize AI-based recommendations when utilitarian attributes are relatively more important (e.g., documentaries) and human-based recommendations (""similar users"") when hedonic attributes are relatively more important (e.g., horror movies).This research also highlights boundary conditions that may prove useful for practitioners. Study 5 indicated that when consumers want recommendations that are matched to their unique preferences, they resist AI recommenders and instead prefer human recommenders, regardless of hedonic or utilitarian goals. These results suggest that companies whose customers are known to be satisfied with ""one size fits all"" recommendations, or who are not in need of a high level of customization, may rely on AI systems. However, companies whose customers are known to desire personalized recommendations should rely on humans. Some companies, such as Amazon, seem to be implementing a similar strategy. Even though most of Amazon's recommendations are based on algorithms, the company has recently started offering an additional service for an added fee called ""personal shopper."" This service relies on human shopping assistants to give clothing recommendations rather than on algorithms. Our results indicate that more companies, especially those in markets that are relatively more hedonic, should follow Amazon's example.Study 6 provides another managerially relevant boundary condition: augmented intelligence. The results of this study indicate that consumers are more receptive to AI recommenders, even in the case of hedonic goals, if the AI recommender does not replace a human recommender but instead assists a human recommender who retains the role of ultimate decision maker. These results are important for practitioners managing relatively more hedonic products or services. For instance, in a personal conversation with the authors, a Walmart marketing manager noted how the top two most frequently ignored recommendations on the company's website are those for alcoholic beverages and food items—arguably products for which hedonic attributes tend to be more salient and important. In these circumstances, practitioners could leverage our results and utilize AI systems to generate an initial recommendation on which a human then ""signs off.""Finally, in Studies 7a–7b we tested an intervention that practitioners managing relatively more hedonic products and relying on AI systems may execute. Building on the consider-the-opposite protocol, we created a realistic chatbot that interacted with participants and nudged them to consider that the AI recommender could be good at things that participants did not expect it to be good at. The intervention was successful in both studies, suggesting that practitioners may utilize this technique if hedonic attributes are important. Limitations and Future ResearchDespite the robustness of the word-of-machine effect, our research has limitations that offer several opportunities for future research. First, there is the possibility that drawing attention to the source of a recommendation primed study participants. AI recommenders might have primed utilitarian attributes or made utilitarian goals more salient, and it was the associated increased activation of these concepts, rather than competence perceptions, that gave rise to the word-of-machine effect. Although possible, this alternative explanation based on priming is unlikely given the results of a study we report in Web Appendix D. In this study (N = 230), we first primed participants with either human or AI-related concepts by drawing their attention to either a human or an AI recommender, thus approximating the kind of priming that could have occurred in our studies. To assess whether the AI recommender primed utilitarian concepts, we then measured perceptions of utilitarian and hedonic attributes of a stimulus in a domain unrelated to one in which the priming manipulation occurred. This stimulus was pretested to be neutral (i.e., perceived to be equally utilitarian and hedonic). The results indicate that the stimulus was perceived to be equally utilitarian and hedonic regardless of the priming manipulation. Although these results offer preliminary evidence that priming does not account for the word-of-machine effect, the inferences one can draw from a null effect are limited. More broadly, the question of whether AI-based recommendations activate specific constructs that might be influential on decision making is a worthy avenue for future research.Second, even though we tested the word-of-machine effect across multiple domains, there remains the possibility that the effect is stronger or weaker in certain categories. For instance, the effect might be stronger in categories (e.g., a chocolate cake) in which discerning hedonic attributes (e.g., how tasty or how indulgent it is) is easier than discerning utilitarian attributes (e.g., how many macronutrients it contains, or how healthy it is). Future research could more systematically investigate what dimensions of different product categories strengthen versus weaken the word-of-machine effect.Third, the lay beliefs underlying the word-of-machine effect may be transitional. As competence perceptions driving the word-of-machine effect are based on a lay belief, they are embedded in a cultural view that may change over time. The lay belief about differential competence perceptions may already be inaccurate, as AI is already utilized in domains that are relatively more hedonic. For instance, AI curates flower arrangements on the basis of customers' past transactions and inferred preferences (1-800-Flowers) and creates new flavors for food companies such as McCormick, Starbucks, and Coca-Cola ([41]).Our research also suggests opportunities for future exploration of this area. First, the word-of-machine effect may have interesting downstream consequences on other responses. For instance, relying on an AI recommender may lead consumers to compensate by adjusting their own choices. Given the belief that AI-based recommendations excel on utilitarian attributes and are weaker on hedonic attributes, consumers may choose from a set of options by paying closer attention to the hedonic attributes of the options, assuming that the options are satisfactory in terms of utilitarian attributes. This ""second-step choice"" is an interesting question to consider in the future.Second, in Studies 7a–b we show preliminary evidence of how lay beliefs toward AI systems could be successfully alleviated through a protocol utilized in the decision making literature. Future research could identify other real-world variables that might have similar attenuating effects, such as domain expertise, involvement, time spent making decisions, or familiarity/repeated use of AI systems. A third fruitful research opportunity would be to explore whether consumers can be persuaded to trust AI systems, even more than humans, in the eventuality that AI systems are sufficiently sophisticated to pass the Turing test. In this vein, future research could identify conditions under which the word-of-machine effect reverses, with AI recommenders being more persuasive than humans for hedonic products.As research on the psychology of automation expands to include developments such as AI, we hope that our findings (especially those of Study 6) will spur further research prioritizing the understanding of the vast potential of AI operating in partnership with humans. More research is also necessary to map out the impact of AI systems across consumption settings. AI-powered technologies will be instrumental in optimizing the customer experience at each phase of the consumer journey by offering products of increasing personalization ([41]). New technologies like image, text, and voice recognition, together with large-scale A/B testing will provide managers with the data necessary for a complete, AI-driven customization of the journey ([41]) and will allow researchers to gather the consumer signals that are produced as a by-product of consumer activities ([38]). We hope that future research will focus on how to harness this great potential of AI for managers and researchers alike.Overall, understanding when consumers will be amenable to and when they will resist AI-driven recommendations is a pressing and complex endeavor for researchers and firms alike. We hope that our research will spur further exploration of this important topic. " 4,Augmented Reality in Retail and Its Impact on Sales," The rise of augmented reality (AR) technology presents marketers with promising opportunities to engage customers and transform their brand experience. Although firms are keen to invest in AR, research documenting its tangible impact in real-world contexts is sparse. In this article, the authors outline four broad uses of the technology in retail settings. They then focus specifically on the use of AR to facilitate product evaluation prior to purchase and empirically investigate its impact on sales in online retail. Using data obtained from an international cosmetics retailer, they find that AR usage on the retailer's mobile app is associated with higher sales for brands that are less popular, products with narrower appeal, and products that are more expensive. In addition, the effect of AR is stronger for customers who are new to the online channel or product category, suggesting that the sales increase is coming from online channel adoption and category expansion. These findings provide converging evidence that AR is most effective when product-related uncertainty is high, demonstrating the technology's potential to increase sales by reducing uncertainty and instilling purchase confidence. To encourage more impactful research in this area, the authors conclude with a research agenda for AR in marketing.","""At some point, we're going to look back and think, how didwe not have a digital layer on the physical world?"" – Greg Jones, Director of VR and AR at GoogleAugmented reality (AR) is a technology that superimposes virtual objects onto a live view of physical environments, helping users visualize how these objects would fit into their physical world. Even though AR is in its early stages of growth, leaders in the field such as Apple's CEO, Tim Cook, and Google's Director of Virtual Reality (VR) and AR, Greg Jones, have lauded its potential to transform the retail experience ([ 3]; [23]). With the launch of AR toolkits by technology giants Apple and Google, it is now easier for companies to develop their own AR-enabled mobile apps. Jumping on the bandwagon, Facebook recently introduced AR-enabled display advertisements for their News Feed ([11]), making the technology even more accessible to companies.Augmented reality (AR) is a technology that superimposes virtual objects onto a live view of physical environments, helping users visualize how these objects would fit into their physical world. Even though AR is in its early stages of growth, leaders in the field such as Apple's CEO, Tim Cook, and Google's Director of Virtual Reality (VR) and AR, Greg Jones, have lauded its potential to transform the retail experience ([ 3]; [23]). With the launch of AR toolkits by technology giants Apple and Google, it is now easier for companies to develop their own AR-enabled mobile apps. Jumping on the bandwagon, Facebook recently introduced AR-enabled display advertisements for their News Feed ([11]), making the technology even more accessible to companies.From a retail perspective, a promising application of AR is to facilitate product evaluation by letting customers experience products virtually prior to purchase. Although research has emphasized the importance of direct product experiences to help customers learn about product benefits and assess product fit ([ 6]; [12]), offering direct product experiences can be a logistical challenge, especially in online retail. The introduction of AR has made it possible for shoppers to experience products virtually in the absence of physical products, managing their expectations and instilling purchase confidence ([44]). For example, Amazon and IKEA are using this technology to help customers determine if products or furniture pieces offered online are compatible with their existing room décor, and L'Oréal and Sephora are using AR to show customers how different cosmetic products would alter their appearance. Some of these applications are illustrated in Web Appendix A.Despite the keen interest in AR, there has been limited research demonstrating its tangible impact in real-world contexts. Understanding the potential for AR to increase revenues is important for justifying investments in this new technology. However, the impact of AR on actual product sales is still ambiguous. By helping customers visualize products in their consumption contexts, AR could reduce product fit uncertainty, resulting in more sales. Conversely, AR may also discourage purchases if it leads to perceptions that the products may not fit well. As the technology is unable to convey experiential product attributes that could be important in purchase decisions (e.g., product texture or scent), the impact of AR on sales could also be insignificant. This uncertainty surrounding the impact of AR has been cited as one of the main reasons why companies are still hesitant to embrace the technology, even though most recognize the exciting opportunities it offers ([ 5]). Echoing this lack of clarity, a recent CNN article regarding applications of AR in the cosmetics industry expressed that ""virtual lipsticks and smokey eye shadows are popular in apps, but are they translating into more makeup sales? Hard data isn't easy to come by"" ([38]).Furthermore, whether and how the impact of AR varies across different products or customer segments is also unclear. Having a more nuanced understanding of how AR affects sales would help marketing managers determine when it would be most appropriate to deploy the technology. Conceivably, if AR increases sales by reducing uncertainty, its impact may depend on product and customer characteristics that influence uncertainty in purchase decisions, such as brand popularity, product appeal, and customers' familiarity with the retail channel or category. Accordingly, the present research adopts the retailers' perspective to examine the following questions: How does the use of AR to facilitate product evaluation impact product sales? How does the sales impact of AR usage differ across product characteristics, such as brand popularity, product appeal, rating, and price? How do customers' prior experiences with the online channel and product category influence the sales impact of AR usage?Given that AR is predominantly available on mobile apps ([43]), we focus on the mobile app platform for our analyses. We obtained data from an international cosmetics retailer that incorporated AR into its mobile app to help customers realistically visualize how they would look when they are using different cosmetic products (e.g., eyeshadows, lipsticks). The data contain sales records for 2,300 products, as well as browsing and purchase histories for 160,400 customers, allowing us to investigate how the sales impact of AR varies by product and customer characteristics. In addition, introduction of the AR feature for two product categories during the observation period provided us with a quasi-experimental setting to examine the impact of AR introduction on category sales.Findings from our research provide preliminary evidence that AR usage has a positive impact on product sales. The overall impact appears to be small, but certain products are more likely to benefit from the technology than others. In particular, the impact of AR is stronger for brands that are less popular and products with narrower appeal, suggesting that AR could level the playing field for niche brands or products (sometimes referred to as products in the ""long tail"" of the product sales distribution; e.g., [ 8]). The increase in sales is also greater for products that are more expensive, indicating that AR could increase overall revenues for retailers. In addition, customers who are new to the online channel or product category are more likely to purchase after using AR, suggesting that AR has the potential to promote online channel adoption and category expansion. These findings provide converging evidence that AR is most effective when product-related uncertainty is high, implying that uncertainty reduction could be a possible mechanism by which AR could improve sales.This article is one of the first to empirically demonstrate the impact of AR on sales and how it varies across product and customer characteristics using real-world data. In doing so, it extends prior studies on AR in the marketing field and represents an initial step in understanding what AR means for marketers and retailers. Beyond influencing sales, AR could transform the way brands reach out to and connect with customers at different stages of the customer journey. In the following section, we provide an overview of AR and elaborate on four ways the technology can be incorporated into brands' marketing strategies to reshape the customer retail experience. Then, we focus specifically on how the use of AR to facilitate product evaluation prior to purchase impacts sales in online retail. To encourage marketing academics to further engage in impactful and managerially relevant research in this area, we conclude with a research agenda that we developed in consultation with industry experts and marketing practitioners. Augmented Reality Augmented Reality TechnologyAugmented reality integrates virtual elements into real-world environments to create alternate perceptions of reality. Using sensors and object recognition capabilities from input devices such as cameras, AR technology scans the physical environment, identifies features in the environment, and superimposes virtual objects (e.g., two- or three-dimensional images or animations, text, sounds) on top of a live view of the real world. By blending virtual elements into physical environments in real time, AR enriches users' visual and auditory perceptions of reality. In most cases, the virtual elements are also responsive to movements or gestures, creating an interactive experience for users.Although AR is often classified together with VR, the two technologies are distinct, both in how they function and the way they are experienced. Unlike AR, which receives input from the real world and adds virtual elements to it, VR immerses users in a completely digital and artificial environment, shutting them out from their surroundings. Due to the disorienting experience of being entirely isolated from the real world and the expensive headsets required ([19]), the appeal of VR has largely been limited to industries with products high in simulated content, such as gaming and entertainment ([13]). In contrast, AR allows users to experience virtual elements without the vulnerability of being blind to the real world. In addition, AR can be experienced directly from handheld devices that users already own (e.g., tablets or smartphones). Thus, AR is rapidly gaining prominence, and close to 100 million U.S. consumers are expected to use the technology regularly by 2022 ([43]). Augmented Reality in RetailThe unique capabilities of AR present marketers with new opportunities to engage customers and transform the brand experience. Drawing on an extensive review of current applications of AR, we identified four broad uses of the technology in retail settings: to ( 1) entertain and ( 2) educate customers, help them ( 3) evaluate product fit, and ( 4) enhance the postpurchase consumption experience. These uses loosely correspond to customers' journey from awareness to interest, consideration, purchase, and consumption, and they may not be mutually exclusive. Next, we elaborate on these four uses and provide a summary with relevant examples in Table 1.[ 6]GraphTable 1. Uses of AR in Retail. 1 Note: URL links to these examples are provided in Web Appendix B. EntertainAR's ability to transform static objects into interactive and animated three-dimensional objects offers new ways for marketers to create fresh experiences to captivate and entertain customers. Besides generating hype and interest, marketers have also used AR-enabled experiences to drive traffic to their physical locations. For example, Walmart collaborated with media companies such as DC Comics and Marvel to bring exclusive superhero-themed AR experiences to their stores by placing special thematic displays in selected outlets. In addition to creating novel and engaging experiences for customers, it also encouraged them to explore different areas within the stores. EducateDue to its interactive and immersive format, AR is also an effective medium for delivering content and information to customers. For instance, to help customers better appreciate their new car models, Toyota and Hyundai have utilized AR to demonstrate key features and innovative technologies in a vivid and visually appealing manner. Retailers can also use AR to help customers navigate stores or highlight relevant product information to influence their in-store purchase decisions. Companies such as Walgreen's and Lowe's have developed in-store navigation apps that overlay directional signals onto a live view of the path in front of users to guide them to product locations and notify them if there are special promotions along the way. EvaluateBy retaining the physical environment as a backdrop to virtual elements, AR also helps users visualize how products would appear in their actual consumption contexts, allowing them to more accurately assess product fit prior to purchase. For example, IKEA's Place app uses AR to give customers a preview of different furniture pieces in their homes by overlaying true-to-scale, three-dimensional models of products onto a live view of the room. Customers can easily determine if a product fits in a given space without the hassle of taking measurements. Fashion retailers Uniqlo and Topshop have also deployed the same technology in their physical stores, offering customers greater convenience by reducing the need for them to change in and out of different outfits. An added advantage of AR is its ability to accommodate a wide assortment of products. By replacing tangible product displays with lifelike virtual previews of products, retailers can overcome the constraints of physical space while still offering customers the opportunity to explore different product options. This capability is particularly useful for made-to-order or bulky products. Car manufacturers BMW and Audi have used AR to provide customers with true-to-scale, three-dimensional visual representations of car models based on customizable features such as paint color, wheel design, and interior aesthetics. These cases exemplify AR's huge potential to increase customers' confidence in their purchase decisions for a variety of products. EnhanceLastly, AR can be used to enhance and redefine the way products are experienced or consumed after they have been purchased. For example, LEGO recently launched several brick sets that are specially designed to combine physical and virtual gameplay. Through the companion AR app, animated LEGO characters spring to life and interact with the physical LEGO sets, creating a whole new playing experience. In a bid to address skepticism about the quality of its food ingredients, McDonald's has also used AR to let customers discover the origins of ingredients in the food they purchased via storytelling and three-dimensional animations.The present research focuses on the use of AR to help customers evaluate products prior to purchase. Specifically, we explore the possibility of leveraging AR to reduce product-related uncertainty in online purchase decisions. To extend prior research on AR in retail (summarized in Table 2), we use real-world data to examine how customers' use of AR to try products (for brevity, we refer to this as ""AR usage"" for the rest of the article) affects product and brand sales. In the following section, we present our conceptual framework and develop hypotheses for the impact of AR usage on sales.GraphTable 2. Selected Literature on AR in Retail. Conceptual Framework Product Uncertainty in Online RetailBecause customers cannot perfectly predict the consequences of their purchase decisions, uncertainty is inherent in market exchanges ([ 4]). However, it is especially pronounced in online environments due to the spatial separation between buyers and sellers as well as the temporal separation between payment and product fulfillment ([ 9]; [41]). Unlike in traditional retail settings, customers are unable to physically inspect or evaluate products before making a purchase, resulting in greater uncertainty that the products would be able to deliver the expected level of performance or benefits ([ 6]; [17]; [36]).Researchers have broadly distinguished between two types of product uncertainty in online markets: product performance uncertainty and product fit uncertainty. Product performance uncertainty occurs when customers are unable to evaluate or predict product performance due to imperfect knowledge ([17]). In contrast, product fit uncertainty occurs when customers are unable to determine if the product matches their needs ([ 6]; [32]). The latter form of uncertainty is typically higher for products with experience attributes (i.e., attributes that can only be evaluated after the product has been experienced; [32]), such as apparel or beauty products.Several mechanisms to reduce product performance uncertainty in online retail have been suggested. For example, retailers could lower information asymmetry by providing diagnostic product descriptions or by including credibility signals such as third-party product assurances, warranties, or customer reviews ([17]; [51]). In contrast, product fit uncertainty typically requires direct product experience to resolve, as it is idiosyncratic in nature and varies from individual to individual. Although some retailers have adopted try-before-you-buy programs (e.g., Warby Parker's home try-on program; [ 6]) or lenient product return policies ([24]; [53]) to provide opportunities for direct product experiences, these measures are notoriously costly for retailers due to the additional shipping and handling costs and risks of product damage ([22]). Furthermore, direct product experiences may not be viable or appropriate for certain products, such as products that are customized (e.g., engagement rings), products that require assembly (e.g., furniture), or personal care products (e.g., cosmetics). Augmented Reality and Product UncertaintyThe introduction of AR has made it possible to substitute direct product experiences with virtual product experiences to facilitate product evaluation and reduce product fit uncertainty. Using a situated cognition perspective, [29] propose that the value of AR lies in its ability to help customers visually integrate virtual products into the real-world environment (i.e., ""environmental embedding"") and use bodily movements and physical actions to control how products are presented (i.e., ""simulated physical control""). The unique combination of these two properties induces perceptions that the virtual products are physically present in the real world, creating realistic product experiences. Consequently, customers are able to evaluate products as if they are actually interacting with the real products, resulting in reduced product fit uncertainty. In line with this, prior research finds that vivid images and greater control over the presentation of information are effective ways to alleviate uncertainty in online environments ([51]). By helping customers visualize products in their consumption contexts and reducing product fit uncertainty, AR-enabled product experiences increase the level of ease customers feel in the decision-making process, translating to positive behavioral intentions ([26]; [29]).However, although AR communicates visual information about products, it is unable to convey other experiential product attributes (e.g., product texture, scent). For example, even though customers may use AR to visualize an IKEA sofa in a room, they are unable to assess how comfortable it is. Similarly, users trying on cosmetic products via AR are unable to evaluate other product attributes such as the texture and consistency of the product, which may affect ease of application and the way the product feels on the skin. According to [34], if customers do not perceive trial experiences as accurately representing actual consumption experiences, they may discount those trial experiences when they form judgments about the product. Thus, the extent to which virtual product experiences involving AR could influence online purchases is unclear. Nevertheless, as prior research has demonstrated the positive effects of providing fit information in online retail (e.g., [21]; [35]), we expect AR usage to have a positive impact on product sales because the technology could convey visual information that may reduce product fit uncertainty in online purchase decisions. Therefore, we predict the following: H1: AR usage has a positive impact on sales.Building on the proposition that AR usage increases sales by reducing product fit uncertainty, we further hypothesize that AR would have a stronger impact when customers experience higher levels of uncertainty. In particular, the level of uncertainty experienced in a purchase decision could depend on product characteristics such as brand popularity, product appeal, and ratings. The level of uncertainty may also influence the price that customers are willing to pay for the product. Thus, the relationship between AR usage and sales may differ across these product characteristics. In addition, customers also vary in their need to reduce product fit uncertainty before making a purchase ([ 6]). This need to reduce uncertainty could depend on customers' familiarity with the online channel and product category. As a result, the impact of AR may also vary across these customer characteristics. Accordingly, we develop hypotheses for the moderating effects of product and customer characteristics in the following sections. Our conceptual framework is presented in Figure 1.Graph: Figure 1. Conceptual framework.Note: Signs in parentheses represent the hypothesized effects. Moderating Effects of Product Characteristics Brand popularityPrior research has shown that consumers are more cautious when they purchase from lesser-known brands, as they anticipate feeling more regret if the product turns out to be inferior ([45]). Consistent with this, [18] find that cultures high in uncertainty avoidance place greater emphasis on brand credibility. In online environments, brand signals are even more important because consumers are not able to inspect products before purchasing ([16]). However, [31] demonstrates that when additional information is available to facilitate decision making, consumers rely less on brand signals. As a result, less-established brands benefit more from the increased availability of information. In the same vein, by communicating visual information to help customers assess product fit, AR may reduce uncertainty in online purchase decisions. Consequently, AR may decrease customers' reliance on brand signals and inadvertently increase preference for brands that are less popular. We use the term ""popular"" in a general sense to refer to brands that are more widely adopted. Therefore, we hypothesize the following: H2a: The impact of AR usage on sales is stronger for brands that are less popular. Product appealWithin the same category or brand, products may also have different levels of appeal due to the alignment between their inherent characteristics and general consumer preferences. For example, a red lipstick is more mainstream and has broader appeal than a blue lipstick. We draw a distinction between brand popularity and product appeal in that the latter depends on intrinsic properties of the product and could be independent of the brand. Thus, a red lipstick from an unknown brand could have broad appeal but low brand popularity, whereas a blue lipstick from a well-known brand could have limited appeal despite having high brand popularity. As products with broad appeal cater to the masses, they are more likely to match the needs of the general consumer. Conversely, because products with narrower appeal serve a niche segment, there is a higher probability that they will not match the preferences of the general consumer and will thus carry greater product fit uncertainty. Nevertheless, [ 8] demonstrate that in online contexts, search and discovery features such as search tools or recommendation engines can shift consumers' preferences to niche products by lowering the cost of acquiring product information. Consistent with this, [50] find that products with narrower appeal benefit more from greater information availability. By visually conveying product information to help customers assess product fit in an effortless and risk-free environment, AR could have a stronger impact for products with narrower appeal due to the higher product fit uncertainty associated with these products. Therefore, we hypothesize the following: H2b: The impact of AR usage on sales is stronger for products with narrower appeal. RatingsCustomers often turn to online ratings or reviews as a source of information to resolve uncertainty about product quality and fit ([14]). In line with this, [37] find that consumers from countries that are high in uncertainty avoidance are more sensitive to both the valence and volume of product ratings. However, as consumers tend to overrate direct experiences with products ([30]), the ability to evaluate products and resolve uncertainty via firsthand experiences with those products on AR platforms may reduce customers' reliance on online ratings. Thus, by enabling customers to learn about product benefits and assess product fit through their own virtual experiences, AR could diminish the role of online ratings in purchase decisions. As a result, customers may be more amenable to purchasing products despite their lower ratings if they are able to try these products using AR. Therefore, we predict the followsing: H2c: The impact of AR usage on sales is stronger for products with lower ratings. PriceWhen customers experience product uncertainty, they are not able to accurately assess the benefits the products offer. As a result, customers are more likely to undervalue the products and are less willing to pay a premium ([17]). Consistent with this, [36] find that customers who are familiar with online shopping are still hesitant to purchase expensive products through the internet when there is a high degree of product uncertainty because they could suffer greater financial losses if these products do not fit them well. By facilitating product evaluation prior to purchase, AR helps customers ascertain if products match their needs and preferences. Consequently, customers may experience less uncertainty and feel more comfortable purchasing products that are more expensive. In line with this, [27] find that AR usage improves decision comfort, leading to higher willingness to pay. Therefore, we predict the following: H2d: The impact of AR usage on sales is stronger for more expensive products. Moderating Effects of Customer Characteristics Channel experienceAccording to [36], customers who are familiar with online shopping are more inclined to purchase products with a higher degree of uncertainty because their cumulative online shopping experiences help them develop the ability to assess products when limited information is available. Thus, customers who have purchased from a retailer's online channel in the past may feel more comfortable making subsequent online purchases despite experiencing product uncertainty, potentially making them less dependent on AR to make their purchase decisions. In contrast, customers who are new to the retailer's online channel (but have made prior purchases at the retailer's offline channel) are not accustomed to making purchases in the absence of actual products. As a result, they may experience greater product fit uncertainty and may be deterred from purchasing online due to the inability to assess product fit. Because AR simulates the in-store experience of trying products, it may help reduce product fit uncertainty for customers who are new to the online channel. These customers may derive greater value from the ability to evaluate products virtually, potentially making them more likely to purchase online after using AR. Therefore, we predict the following: H3a: The impact of AR usage on sales is stronger for customers who are new to the retailer's online channel. Category experienceBesides channel experience, customers' familiarity with the product category also affects their level of product fit uncertainty ([32]). Customers who are familiar with a product category can draw on their prior experiences as an information source to form judgments about products ([46]). As a result, they may rely less on AR in their purchase decisions. Conversely, customers who are unfamiliar with a product category lack the necessary category knowledge to evaluate product attributes and, at the same time, may not be aware of their own preferences ([32]). Consequently, these customers will have more difficulty assessing whether a product's attributes match their preferences, resulting in greater product fit uncertainty. By helping customers visualize how products would appear in their actual consumption contexts, AR could reduce product fit uncertainty and increase purchase confidence for customers who are new to the product category. As a result, AR usage may have a stronger impact on the purchase decisions for these customers. Therefore, we predict the following: H3b: The impact of AR usage on sales is stronger for customers who are new to the product category.To summarize, we propose that AR usage will positively impact sales by reducing product uncertainty. Following this line of reasoning, we developed several predictions about which products would be more likely to benefit from AR and which customers would be more likely to respond to AR. Empirical Analysis Empirical ContextAs AR is predominantly available through mobile apps ([43]), we focus our analyses on the mobile app platform. To test our hypotheses, we obtained data from an international cosmetics retailer with both an online and offline presence. Leveraging AR technology, the retailer integrated a new feature on its existing mobile app that allowed customers to virtually try on makeup products (e.g., eyeshadows, lipsticks). The AR technology detected customers' facial features via their smartphone cameras and superimposed the shade of chosen products onto a live view of their face in real time, giving them a realistic visual representation of their appearance when they are using the products. The brand, product name, and price were displayed at the top of the screen. Figure A3 in Web Appendix A provides a visual example of a customer trying on a lipstick using the AR feature. For comparison, the corresponding product detail page view (i.e., the conventional way of conveying product-related information on mobile retail apps) is also provided. Prior to the start of our observation period in December 2017, the AR feature was only available for lip categories (i.e., lipstick and lip gloss) and was subsequently introduced for eye categories (i.e., eyeshadow and eyeliner) in March 2018. Figure A4 in Web Appendix A provides a visual overview of AR availability for the different categories.We obtained two separate data sets from the retailer for one of its key markets in Asia Pacific. The first data set contained information about browsing activities on the mobile app, including specific products customers tried using the AR feature, and covered a 19-month period from December 2017 to June 2019. The second data set contained transaction records from June 2017 to June 2019 for all retail channels, including mobile app, website, and offline stores. We merged the two using customers' loyalty card number, which allowed us to match AR usage and product purchases at a disaggregate level.During the 19-month period, a total of 160,407 shoppers browsed products from the lip and eye categories across 806,029 sessions, 20.8% of which involved AR usage. Customers who used AR during the session spent 20.7% more time browsing (Mwith_AR = 16.6 minutes, Mwithout_AR = 13.8 minutes, p <.01) and browsed 1.28 times more products (Mwith_AR = 53.9, Mwithout_AR = 42.2, p <.01). The purchase rate for sessions with AR usage was 19.8% higher than for sessions without AR usage (3.15% with AR vs. 2.63% without AR, p <.01), providing preliminary indication of the positive impact of AR on sales.We divide our analyses into three sections. In the first section, we perform the analysis at the product level to examine the moderating effects of brand popularity, product appeal, rating, and price. To minimize selection bias arising from availability of the AR feature, we focus on lipsticks and lip glosses, as the feature was available for > 96% of products in each of these categories. In the second section, we take advantage of the introduction of AR for two eye categories (i.e., eyeshadow and eyeliner) to examine the effect of AR introduction on category sales using a quasi-experimental differences-in-differences-in-differences (DDD) approach. Finally, we investigate how the impact of AR varies at the customer level. As all customers had no knowledge that the AR feature would be introduced for the two eye categories prior to the introduction, the event provided us with a clean setting for examining how customers' channel and category experience (prior to the introduction) would moderate the impact of AR usage on purchase probability. Product-Level AnalysisAs product color is an important factor in cosmetic purchases, we considered each shade/color of retail merchandise as a unique product. In total, we had 2,334 products in the lipstick and lip gloss categories (1,984 products across 41 brands for lipstick; 350 products across 28 brands for lip gloss). Our empirical strategy was to relate the number of customers using AR to try each product during a particular time period with sales volume for that product during the same time period. We estimated the model at the monthly product level, giving us a total of 44,346 observations (2,334 products × 19 months from December 2017 to June 2019). As one of our objectives was to examine the moderating effect of product ratings, we included products with a rating in the main analysis and replicated the analysis for all products as a robustness check. Our final sample for the main analysis consisted of 29,345 observations. Model specificationFor each product i, we modeled how the volume of AR usage in month t, AR Usageit, influenced the number of products sold in month t, Product Salesit. As Product Salesit was a count variable with significant over-dispersion (M =.46, SD = 1.73) and over 80% of observations were 0, we used a zero-inflated negative binomial model for the estimation. The vector of covariates in the regression is given by the following equation: Xitβ =β0+ β1AR Usageit+ β2Brand Popularityit+ β3Appealit+β4Ratingit+β5Priceit+ β6AR Usageit×Brand Popularityit+ β7AR Usageit×Appealit+ β8AR Usageit×Ratingit+ β9AR Usageit×Priceit+ β10Categoryi+∑m = 1T − 1δmMontht+∊it. Graph1In Equation 1, we measured AR Usageit as the number of customers using AR to try product i during month t. As brands that are more widely adopted should have higher sales, and because the web and app channels are both online and carry identical products, we used total brand sales (within the category) from the web channel during the same period as a proxy for brand popularity, Brand Popularityit. Following prior research using product sales as an indicator of mass or niche appeal (e.g., [ 8]), we used total product sales from the web channel during the same period to reflect product i's breadth of appeal, Appealit. Ratingit and Priceit were the rating and price of product i at time t, respectively. To examine how the impact of AR was influenced by brand popularity, product appeal, rating, and price, we included the corresponding interactions in the model. In addition, we included Categoryi (1 = lipstick, 0 = lip gloss) and a series of dummy variables, Montht, (for t = 1,..., T months) to control for category and month effects. Table 3 provides a summary of how the variables were operationalized and their descriptive statistics, and we provide their correlations in Web Appendix C. All the correlations were low, and the variance inflation factors were below 1.62, indicating that multicollinearity was not an issue. To prevent overestimation of effects due to the panel structure of the data, we clustered standard errors at the product level (e.g., [49]).GraphTable 3. Variable Operationalization and Descriptive Statistics for Product Model. Identification strategyOur objective was to understand how the volume of AR usage for product i during month t, AR Usageit, influenced product sales, Product Salesit. However, AR Usageit could be endogenous, as customers may have been more inclined to use AR to try products they were already interested in purchasing. To account for this endogeneity, we used the two-stage residual inclusion method ([48]), which has been used in recent research when both the endogenous and dependent variables are nonlinear (e.g., [ 2]; [15]).Following the two-stage residual inclusion method, we first regressed the endogenous variable, AR Usageit, on all other covariates in Equation 1. Residuals from this first stage were then included to estimate Product Salesit. Similar to the control function approach ([42]), the included residuals controlled for the portion of the endogenous variable that would otherwise correlate with the error term in Equation 1. According to [48], we needed to include instruments in the first stage estimation to resolve the identification problem in Equation 1. These instruments should ( 1) be strongly related to the endogenous variable and ( 2) not be correlated with the error term in Equation 1. In other words, the instruments should only have an indirect relationship with the outcome variable, Product Salesit, through their association with the endogenous variable, AR Usageit. As realizations of the same variable from different markets can serve as suitable instruments ([40], p. 601), we used the volume of AR usage in two other countries for the same product during the same month as our instruments (i.e., AR UsageitCountry_A and AR UsageitCountry_B, respectively). Underlying this choice of instruments is the assumption that customer preferences are similar across markets and that product-specific factors affecting customers' interest in trying products using the AR feature should be constant in all markets, satisfying the first condition. However, the number of customers using the AR feature to try products in other markets should have no bearing on customers' purchase decisions in the focal market, satisfying the second requirement. We also used lagged values of AR Usageit as an alternative instrument (e.g., [15]) and discuss this further in the robustness analyses section.Because AR Usageit is a count variable with significant over-dispersion (M = 13.9, SD = 22.7), we used a negative binomial model for the first stage estimation. As predicted residuals from the first stage were used in the estimation of Equation 1, standard errors needed to be corrected to account for this additional source of variation ([42]). We implemented the cluster bootstrapping method ([10], p.327) to approximate the correct standard errors using 1000 bootstrap samples. ResultsFrom the first stage estimation (provided in Web Appendix D), coefficients for the instruments are positive and significant (.414 for AR UsageCountry_A and.301 for AR UsageCountry_B, p <.01 for both). Furthermore, the instruments are highly correlated with AR Usage (.75 for AR UsageCountry_A and.64 for AR UsageCountry_B, p <.01 for both), and the F-statistic of excluded instruments in the first stage regression is 5,520, which is well above the recommended cutoff of 10 ([ 1]). These results indicate that the instruments are strongly related with the endogenous variable. To assess validity of the instruments, we performed the Hansen J-test for over-identifying restrictions. Results from the test fail to reject the null hypothesis that the instruments are uncorrelated with the second stage error term (χ2 ( 1) =.699, p =.40), providing additional support for the choice of instruments.To examine the main effect of AR usage in H1, we estimated the second stage model without interaction terms. Results for this model are presented in Table 4, Column 1. The coefficient for AR Usage is significantly positive (.006, p <.01), suggesting a small but positive relationship between the number of customers using AR to try the product and sales for that product during the same month. Thus, H1 is supported. The coefficients for other variables are largely in line with common intuition. For example, brand popularity (.894, p <.05), breadth of product appeal (.385, p <.01), and product rating (.094, p <.05) are positively associated with product sales, whereas price (−.005, p <.10) has a negative relationship with product sales. The coefficient for the residual correction term, which is equivalent to the Hausman test for the presence of endogeneity ([40]), is significant (.071, p <.01), indicating that the endogeneity-corrected estimates are preferred. Thus, we focus on results from the two-stage model and provide results for the uncorrected model in Web Appendix D.GraphTable 4. Product Model: Impact of AR Usage on Product Sales and Moderating Effects of Product Characteristics. 2 *p ≤.10; **p ≤.05; ***p ≤.01.3 Note: Standard errors (clustered at product level) are in parentheses.Results for the full second stage model are presented in Table 4, Column 2. In support of H2a and H2b, the interactions between AR Usage and Brand Popularity (−.022, p <.05) and Appeal (−.001, p <.01) are significantly negative, indicating that the sales impact of AR usage is stronger for brands that are less popular and products with narrower appeal. The interaction between AR Usage and Price is significantly positive (.000, p <.10), suggesting that the sales impact of AR usage is stronger for products that are more expensive. Thus, H2d is also supported. However, the results do not provide support for H2c, as the interaction between AR Usage and Rating is not significant (.001, p >.10). Robustness analysesWe performed several analyses to ensure that our findings are robust to different assumptions and model specifications. First, following prior research, which has used lagged values of endogenous variables as instruments (e.g., [15]), we used the volume of AR usage for product i in the past one month as an alternative instrument. As app activity data prior to the first month (i.e., December 2017) was unavailable, we excluded observations for the first month. Results for this model are presented in Table 4, Column 3, and the findings are consistent. Because we were interested in the moderating effect of ratings, we focused on products that had a rating in the main analysis. Since the coefficient for Rating was not significant, we excluded it in the model specification and replicated the analysis for all products. Results for this model are also consistent with the main findings and are presented in Table 4, Column 4.We also explored alternative operationalizations for AR Usage, Brand Popularity, and Appeal. Instead of operationalizing AR Usage as the number of customers using AR to try product i, we used the number of times product i was tried using AR to account for repeated AR usage from the same customer. We also operationalized Brand Popularity and Appeal as the number of customers purchasing the brand and product, respectively. Results for these models are reported in Web Appendix E. Across all robustness analyses, results are generally consistent with the main model, providing further validation for our findings. Category-Level DDD AnalysisThe introduction of AR for two eye categories (i.e., eyeshadow and eyeliner) in mid-March 2018 presented a unique opportunity to examine the impact of AR introduction on sales. Using a quasi-experimental approach, we regarded AR introduction as a treatment and examined its impact by comparing differences in sales for products with and without the AR feature, before and after the feature was introduced. Because the AR feature was only available for eyeshadows and eyeliners, a potential comparison could be between these categories and other eye categories that did not have the feature (i.e., eyebrows, mascaras, and eye palettes). This between-category comparison relies on the crucial assumption that sales trends across different eye categories would be parallel in the absence of AR introduction. As cosmetic products are often used concurrently, sales for products targeting the same facial feature should generally move in the same direction. Since the AR feature was only available on the mobile app, an alternative comparison could be between the app and web channels. This approach avoids the assumption that trends across different eye categories are similar, but it requires a separate assumption that without AR introduction, sales trends in the two online channels would be parallel.A more robust approach that does not require either of these assumptions is the DDD approach ([ 1], p. 181; [54], p.150), which combines both comparisons. Specifically, the DDD analysis measures differences between app and web sales for eyeshadows and eyeliners before and after AR introduction, relative to the same differences for other eye categories that do not have the AR feature. Thus, the DDD approach controls for both channel and category trends that could potentially confound the effect, and it relies on the more relaxed assumption that in the absence of AR introduction, sales trends in the two online channels would be parallel for products in the same category. Following [33] and [20], we conducted two falsification tests using data from the pre–AR introduction period, and the results provide support that this assumption holds in our study. Details and results for these falsification tests are included in Web Appendix F.Accordingly, we examined changes in weekly sales for the five product categories (i.e., eyeshadow, eyeliner, eyebrows, mascara, and eye palettes) across two channels (i.e., app and web) before and after AR introduction. Our sample covers a duration of 108 weeks (i.e., 42 weeks for the pre–AR introduction period and 66 weeks for the post–AR introduction period), giving us a total of 1,080 observations (5 × 2 × 108 = 1,080). Model specificationThe outcome variable of interest was sales for category j on channel k during week t, Category Salesjkt. As Category Salesjkt was a count variable with significant overdispersion (M = 64.8, SD = 78.0), we used a negative binomial model for the estimation. Following [54], p.150), the vector of covariates in the regression is given by the following:  Xjktβ=β0+β1AR Introt+β2Appk+β3AR Featurej+ β4AR Introt×Appk×AR Featurej+ β5AR Introt×Appk+ β6AR Introt×AR Featurej+ β7Appk×AR Featurej+∑c = 1J − 2γcCategoryj+∑w = 1T − 2δwWeekt + ∊jkt. Graph2In Equation 2, AR Introt is a dummy variable with a value of 1 if week t was in the post–AR introduction period and 0 otherwise. Appk is a dummy variable with a value of 1 for the mobile app and 0 for the website, and AR Featurej is a dummy variable with a value of 1 for eye categories with the AR feature (i.e., eyeshadow and eyeliner) and 0 for other eye categories. The key coefficient of interest was β4, which captured the three-way interaction between AR introduction, retail channel, and categories that have the AR feature. Thus, β4 represents the additional change in mobile app sales post–AR introduction for eyeshadow and eyeliner, after accounting for channel and category-related changes over the same period (captured by β5 and β6 respectively). We included all lower-order interactions in the model, as well as a series of dummy variables, Categoryj (for j = 1,..., J categories) and Weekt, (for t = 1,..., T weeks) to control for category and week effects. Because Categoryj was perfectly collinear with AR Featurej and Weekt was perfectly collinear with AR Introt, we excluded dummy variables for an additional category and week. To account for the panel nature of the data, we clustered standard errors at the category-channel level, allowing errors for observations from the same category within each channel to correlate. ResultsBefore discussing results for the DDD analysis, we present the basic pre-post model in Table 5, Column 1. We regressed weekly mobile app sales for eyeshadow and eyeliner on AR Introt and the vector of dummies. The coefficient for AR Introt is significantly positive (.611, p <.05), providing preliminary evidence that sales increased after AR was introduced. Results for the DDD analysis are presented in Table 5, Column 2. The coefficient for the three-way interaction between AR introduction, app, and categories with the AR feature is marginally significant (.449, p <.10), providing some evidence that sales for eyeshadows and eyeliners increased on the app channel after AR was introduced.GraphTable 5. DDD Analysis: Impact of AR Introduction on Category Sales. 4 *p ≤.10; **p ≤.05; ***p ≤.01.5 Note: Standard errors (clustered at category-channel level) are in parentheses. Robustness analysesTo check the DDD identification strategy, we included channel and category trends in Equation 2 ([ 1], p. 178). Results of this alternative model are presented in Table 5, Column 3, and the coefficient of the three-way interaction of interest is similar in direction, magnitude, and significance with the main model. Although the weekly fixed effects controlled for variations in overall sales between weeks, they did not account for time-varying confounding effects that were specific to the channel-category. Thus, if there were more app-exclusive sale events for the eyeshadow and eyeliner categories in the post–AR introduction period relative to the pre–AR introduction period, the effect of AR Introduction on app sales in these two categories would be overstated. As a robustness check, we removed weeks that coincided with sale events from the analysis and present the results in Table 5, Column 4. We also split AR Featurej into the two eye categories with the AR feature, Eyeshadowj and Eyelinerj, and the coefficients of both three-way interactions are marginally significant, providing convergent validity for our results. Furthermore, results from the Wald test for equality of coefficients fail to reject the null hypothesis that the coefficients are the same (p =.76), indicating that the effect of AR introduction on sales is not category-specific. Lastly, we estimated the same model using a Poisson regression. Results of these additional analyses are provided in Web Appendix G. Across all robustness analyses, the direction, magnitude, and significance of coefficients are similar to the main model.Overall, the results provide additional support for H1 and demonstrate that the positive impact of AR generalizes to other product categories. We note that because the retailer did not announce the introduction of AR for the eye categories, usage of the feature was low. On average, the weekly number of customers using AR to try products from the eye categories was 6.4 times lower than the number for lip categories (Meyes = 271.14 vs. Mlips = 1,737.00). Thus, our result is a conservative estimate of the impact of AR introduction, and we speculate that the effect could have been larger if the retailer had advertised the feature. To establish a direct relationship between AR usage and purchase, and to further examine the moderating effects of customers' channel and category experience, we next turn our attention to the customer level. Customer-Level AnalysisWe focused on the sample of active customers (i.e., those who made a purchase in the past one year) who browsed products in the eyeshadow or eyeliner categories during the 12-month period[ 7] after the retailer introduced AR for these two categories (i.e., mid-March 2018 to mid-March 2019). In total, our sample included 42,493 customers. At the time of AR introduction, 40.2% of these customers had never purchased online before (i.e., new to the online channel) and 43.4% had never purchased eyeshadow or eyeliner before (i.e., new to the categories). During the 12-month period after the retailer introduced the AR feature for the two categories, 13.9% of customers used the feature to try eyeshadows and eyeliners, and 15.0% purchased at least one product from these categories using the app. Accordingly, we modeled how AR usage influenced customers' probability of purchasing products from these two categories in the focal period. Model specificationThe dependent variable of interest, P(Purchaseieyes), was customer i's probability of purchasing at least one eyeshadow or eyeliner on the app within 12 months of AR introduction for the two categories. As the dependent variable was binary, we used a probit model with the following specification: P(Purchaseieyes= 1|Xiβ)= Φ (β0+ β1AR Usageieyes                           + β2New Channeli+ β3New Categoryi                           + β4AR Usageieyes× New Channeli                           + β5AR Usageieyes× New Categoryi                           + Browsingi'γ + PastPurchasei'δ + ∊i). Graph3In Equation 3, Φ denotes the standard probit link function. AR Usageieyes represents the focal independent variable and takes a value of 1 if customer i used the AR feature to try eyeshadows or eyeliners during the period and 0 otherwise. New Channeli and New Categoryi are both indicator variables representing customers' (lack of) prior experience with the channel and category. New Channeli takes a value of 1 if customer i is new to the online channel and 0 otherwise, and New Categoryi takes a value of 1 if customer i is new to the two eye categories and 0 otherwise. To examine how these two variables moderate the effect of AR usage on purchase, we included interactions between the variables and AR Usageieyes. We also included a vector, Browsingi, to control for customers' browsing behavior before and during the focal period to account for customer interest and engagement. Because the browsing activity data set starts at December 2017 (i.e., three months prior to the introduction of AR for the eye categories), we used a three-month window for past browsing behavior. Lastly, we included a vector, Past Purchasei, to control for customers' purchase history in the 12 months prior to AR introduction for the eye categories to account for customer loyalty. Table 6 provides a summary of how the variables are operationalized and their descriptive statistics. The correlations are provided in Web Appendix H, and the variance inflation factors are below 1.75, indicating that multicollinearity is not an issue.GraphTable 6. Variable Operationalization and Descriptive Statistics for Customer Model. 6 Notes: ""Eye products"" refers to eyeshadow and eyeliner, the two categories of interest. The focal period is 12 months after the retailer introduced AR for eye products (i.e., March 15, 2018, to March 15, 2019). Identification strategyAs customers who already intend to purchase products may be more likely to try them using the AR feature, we used the two-stage residual inclusion method to account for this self-selection bias. We used customers' past AR usage for lip products (prior to AR introduction for the eye categories) as the instrument. Customers who had used AR to try lip products in the past were already aware of the feature and could have been more likely to use it again to try eye products. Conversely, customers who had never used the feature to try lip products in the past may have been unaware of it. Because the retailer did not announce the AR introduction for the eye categories, these customers could still have been unaware of the feature. As a result, they would have been less likely to use it to try eye products. Furthermore, because lip and eye products target different areas of the face, past usage of the AR feature to try lip products should not have directly affected the probability of purchasing eye products during the focal period. Thus, we included Past AR Usageilips as an instrument in the first stage to estimate customer i's likelihood of using AR in the focal period. The variable was coded as 1 if customer i used the AR feature to try lip products in the three months before AR was introduced for the eye categories and 0 otherwise. Residuals from the first-stage estimation were then included in Equation 3 to estimate P(Purchaseieyes). Similar to the product model, we bootstrapped 1,000 samples to obtain the proper standard errors. To examine if the findings are robust to alternative identification strategies, we also adopted the propensity score weighting approach, which does not rely on instruments. We discuss this further in the robustness analyses section. ResultsThe coefficient of the instrument in the first stage estimation (provided in Web Appendix I) is positive and significant (.176, p <.01), and the F-statistic of excluded instrument in the first stage regression is 15.8, providing evidence for the strength of the instrument. However, the coefficient for the residual correction term, which is equivalent to the Hausman test for the presence of endogeneity ([40]), is not significant, suggesting that endogeneity may not be a concern. We also used the Heckman selection method ([25]) as an alternative identification strategy, and the inverse Mills ratio is similarly not significant. Therefore, we report estimates for the uncorrected model in the results section and provide the full result for both the two-stage residual inclusion and Heckman selection methods in Web Appendix I. We note that across all models, the substantive findings of interest remain consistent.Table 7, Column 1, displays the results for the model without interactions, representing factors influencing the purchase of eyeshadows or eyeliners during the 12 months after AR introduction for these categories. The coefficient of AR Usageeyes is positive and significant (.046, p <.05), providing further evidence for H1. The coefficients of other variables are largely in line with expectations. For example, the coefficient for New Channel (−.329, p <.01) and New Category (−.120, p <.01) are significantly negative, indicating that customers who are new to the online channel or product category are less likely to make a purchase. The number of orders (.007, p <.01), average order value (.002, p <.01), and number of eye products purchased in the past (.080, p <.01) are positively related to probability of purchasing eye products. Furthermore, total browsing duration (.000, p <.01) and number of eye product pages viewed (.007, p <.01) are also positively related to the purchase of eye products.GraphTable 7. Customer Model: Impact of AR Usage on Probability of Purchase and Moderating Effects of Customer Characteristics. 7 *p ≤.10; **p ≤.05; ***p ≤.018 Note: Standard errors are in parentheses.Table 7, Column 2, provides results for the 12-month model, including interactions. The interaction between AR Usageeyes and New Channel is positive and significant (.091, p <.05), suggesting that AR has a stronger effect among customers who had never purchased online in the past. The average marginal effect of AR usage for customers who are new to the online channel is significantly positive (.018, p <.01), but this effect is not significant for existing online customers (.004, p =.59). Thus, H3a is supported. While the interaction between AR Usageeyes and New Category is marginally significant (.082, p <.10), the average marginal effect of AR usage is significantly positive for customers who are new to the product category (.019, p <.01) and not significant for existing category customers (.003, p =.65), providing support for H3b as well.To understand how the impact of AR changes over time, we repeated the same analysis using a six-month and three-month window, presented in Columns 3 and 4 of Table 7. We find that the interactions between AR Usageeyes and both New Channel and New Category become stronger over time. Although both interactions (as well as the average marginal effects) are insignificant in the 3-month period, the interaction with New Channel becomes significant in the 6- and 12-month period, and the interaction with New Category becomes marginally significant in the 12-month period. Similar to the 12-month model, the average marginal effects in the 6-month model are significantly positive for customers who are new to the online channel (.022, p <.01 vs..006, p =.44 for existing online customers) and product category (.019, p <.05 vs..007, p =.31 for existing category customers). These results suggest that customers may require some time to become comfortable with the technology before using it to make purchase decisions. In addition, the results also imply that the impact of AR does not wear out over time, which rules out novelty effects as an alternative explanation. Robustness analysesResults for the two-stage residual inclusion and Heckman selection methods for all 3-, 6-, and 12-month periods are provided in Web Appendix I. To further examine if the findings are robust to alternative identification strategies, we applied the propensity score weighting approach. We used the first stage equation to calculate customers' propensity for using AR in the focal period and include this as weights in the estimation of Equation 3, following [ 6]. The results are consistent with the main model and are also reported in Web Appendix I.We also examined if the findings are robust to alternative variable operationalizations. First, instead of the probability of purchasing eye products, we used the number of eye products purchased during the focal period as an alternative dependent variable. Second, we replaced the binary AR Usage variable with the number of sessions involving AR usage during the focal period. Third, as alternative measures of channel and category experience, we used the number of online transactions and number of eye products purchased prior to AR introduction for the eye categories, respectively. Findings from these models are consistent, and the results are presented in Web Appendix J. DiscussionAlthough firms are keen to invest in AR, research demonstrating its impact in real-world contexts is limited. The present research provides some preliminary confirmation that both the availability and usage of AR have a small but positive impact on sales. Taken together, our findings provide converging evidence that AR is most effective when product-related uncertainty is high, indicating that uncertainty reduction could be a possible mechanism through which AR could improve sales. Nevertheless, we do not find a significant moderating effect for product ratings, suggesting that even though AR may reduce product fit uncertainty, it may still be unable to compensate for the higher performance uncertainty associated with products that have lower ratings.[ 8] Although we have adopted instrumental variable and quasi-experimental approaches to address endogeneity that is inherent in observational data, we acknowledge that these findings should be viewed as evidence based on correlations, with attempts to come close to causality. Research Implications Augmented reality and product preferenceComplementing past research that has explored how website features drive sales for niche products (e.g., [ 8]; [50]), we show that AR can increase preference for products or brands that are less popular. Thus, retailers carrying wide product assortments can use AR to stimulate demand for products in the long tail of the sales distribution. AR may also help level the playing field for less popular brands. With the launch of AR-enabled display ads on advertising platforms such as Facebook and YouTube, less-established brands could consider investing in this new ad format, as they stand to benefit most from this technology. Retailers selling premium products may also leverage AR to improve decision comfort and reduce customers' hesitation in the purchase process. Augmented reality and category salesWe find that the impact of AR is stronger for customers who are new to the product category, suggesting that AR could increase sales via category expansion. However, because AR seems to be most effective when the level of uncertainty is high, its impact may diminish over time as customers become more familiar with the product category and experience less uncertainty.[ 9] Nevertheless, the finding that AR has a stronger impact for products that are more expensive suggests that, beyond increasing unit sales, AR can also improve category revenues by encouraging customers to purchase products with wider margins. Thus, investments in deploying AR in retail could pay off in the long run. Augmented reality and channel choiceCompared with customers who are already familiar with purchasing online, we find that AR has a stronger effect for customers who are new to the online channel. As prior research has shown that multichannel customers are more profitable ([39]), omnichannel retailers can use AR to encourage their offline customers to adopt the online channel. Given that AR increases online sales among customers who are new to the channel, a potential concern is that AR could lead to cannibalization of sales from offline channels. To understand if the increase in app purchases that we observed was happening at the expense of other sales channels, we ran the same model in Equation 3 but replaced the dependent variable with the probability of purchasing eye products in the web and offline channels (results reported in Web Appendix K). We did not find evidence to indicate that offline customers who use AR (on the app) are more likely to purchase from the web, suggesting that the impact of AR is specific to the app platform. Interestingly, we find that offline customers who use AR are more likely to purchase from the offline channel in the three-month model but not in the six and 12-month model. Thus, contrary to our expectations, the results suggest that AR could have a positive spillover effect to the offline channel, at least in the short run. An Agenda for Future ResearchComplementing prior research, which has predominantly studied AR from a consumer perspective, our research extends the literature by examining what AR means for retailers. To encourage the academic community to produce more impactful research in this nascent field, we developed a research agenda for AR in marketing, with an emphasis on identifying research topics that have strong managerial relevance for industry practitioners. Drawing on a review of the academic literature (e.g., [52]) and recent advancements in AR technology, we generated a list of potential research topics and synthesized these topics into five themes. Next, we consulted two senior marketing practitioners and two academics with expertise in this area to review the research themes and associated topics, and we refined the list according to their feedback.To determine the practical importance of each research theme, we conducted an online survey with 36 marketing practitioners from companies that were using (or planning to use) AR in their marketing, advertising, or retailing activities. Survey respondents first independently rated each research theme in terms of importance to business performance (see [47]) before ranking the five research themes from most to least important. To avoid primacy and recency effects, the order of research themes was randomized across respondents. The mean rating (ranging from 5.1 to 5.8 on a seven-point scale) and ranking scores (from 1 to 5; lower number reflects higher importance) are inversely proportional, demonstrating internal consistency. Web Appendix L provides details for the survey, including survey design, respondent recruitment, and background of respondents.Table 8 presents the research agenda for AR in marketing, comprising the five research themes (ordered by practical importance) and potential topics that could be explored under each theme. Given the novelty of the technology, marketers were primarily concerned with how different design features could be configured to create more effective AR experiences for consumers. For example, greater clarity is needed regarding factors that affect AR experiences, such as fidelity (i.e., how closely virtual objects resemble real objects), motion (i.e., static vs. animated virtual objects), spatial presence (i.e., the feeling that virtual objects exist in a physical space), and embodiment (i.e., the ability to use bodily movements to control virtual objects), and how these can be delivered on AR interfaces. Beyond visual and auditory senses, how haptic feedback (e.g., emission of vibrations on devices to stimulate the sense of touch) influences AR experiences is also of interest.GraphTable 8. Research Agenda for AR in Marketing. 9 Notes: Research themes are ordered by importance on the basis of surveys with 36 marketing practitioners. Details of the survey are provided in Web Appendix L. ""Rating"" refers to the mean importance rating score (on a seven-point scale). ""Ranking"" refers to the mean importance ranking (from 1 to 5; lower number reflects higher importance).Another important consideration is how AR fits into companies' overall marketing strategy. Specifically, marketers would like to know how they can better integrate AR at different stages of the customer journey to increase brand engagement, build emotional connections, and improve relationships with customers. There is also ambiguity regarding the synergy between AR and other elements of the marketing communications mix (e.g., advertising, sales promotions), as well as the effectiveness of product placements and pop-up stores in AR-enabled virtual environments. In particular, the potential for this new technology to complement or replace existing communication and retail channels is still uncertain. As most recent applications of AR have focused on consumer products, marketers also need more guidance on how AR can be appropriately deployed in service industries, such as the tourism and hospitality sector.Besides these two key areas, other worthwhile avenues to explore include the impact of AR on consumer behavior (e.g., cognitive functions, rational decision making, and brand perceptions), how marketers can promote wider adoption of AR, and how the technology can be used to generate valuable marketing insights. Although our research agenda focuses on AR, we note that the research themes could be broadened to encompass other extended realities (i.e., virtual reality and mixed reality).In conclusion, we believe that the marketing community would benefit from a deeper investigation of virtual experiences and their role in marketing. We are excited about where this field is heading, and we look forward to more insightful research to reinforce our understanding of the profound impacts of these new technologies in the marketing domain. " 5,Befriending the Enemy: The Effects of Observing Brand-to-Brand Praise on Consumer Evaluations and Choices," Consumers have grown increasingly skeptical of brands, leaving managers in a dire search for novel ways to connect. The authors suggest that focusing on one's relationships with competitors is a valuable, albeit unexpected, way for brands to do so. More specifically, the present research demonstrates that praising one's competitor—via ""brand-to-brand praise""—often heightens preference for the praiser more so than other common forms of communication, such as self-promotion or benevolent information. This is because brand-to-brand praise increases perceptions of brand warmth, which leads to enhanced brand evaluations and choice. The authors support this theory with seven studies conducted in the lab, online, and in the field that feature multiple managerially relevant outcomes, including brand attitudes, social media and advertising engagement, brand choice, and purchase behavior, in a variety of product and service contexts. The authors also identify key boundary conditions and rule out alternative explanations, further elucidating the underlying mechanism and important implementation insights. This work contributes to the understanding of brand perception and warmth, providing a novel way for brands to connect to consumers by connecting with each other.","In 2017, a popular video gaming brand, Xbox, openly congratulated its competitor, Nintendo, on the launch of its new Switch gaming system ([53]). A few months later, The New York Times encouraged readers to read other news sources such as The Wall Street Journal ([20]). And, in responding to a playful challenge from Kit Kat, Oreo disarmed the brand by communicating how truly irresistible Kit Kat is ([62]). Conventional wisdom fervently advises that ""even mentioning your competition is a bad idea"" ([51]), so why have these brands not only mentioned their competitors but praised them?In a world where brands are trying hard to connect with consumers, many of whom have grown increasingly skeptical of marketers' intentions ([21]), it may be that praising the competition provides unexpected benefits. Our research explores the consequences of brand-to-brand praise—when a brand communicates positively and publicly about another brand. We argue that consumers who observe a brand praising a competitor will believe that the brand has positive intentions toward others, also known as brand warmth, which heightens consumer evaluations and interest in the brand giving the praise.As an introductory illustration of this idea, we scraped data from the Twitter pages of Nintendo and its fiercest competitors, Xbox and PlayStation, around the time of the Nintendo Switch launch in 2017. We found a greater number of likes and retweets (in fact, over ten times as many), as well as more positive sentiment among consumers' comments, when Xbox and PlayStation praised Nintendo for the launch compared with all other types of messages (Web Appendix A). Such preliminary field data motivate our exploration into whether, when, and why brand-to-brand praise affects consumer reactions to brands.Across seven studies, we examine the effects of brand-to-brand praise on consumer attitudes and behavior versus more common forms of brand messaging (e.g., self-promotion or providing helpful information to consumers) and identify important boundaries. In doing so, this research offers several contributions.First, we contribute to the brand perception literature by demonstrating how consumers' perceptions of brands are affected by a brand's interactions with other brands. Prior work has focused on how brand-to-consumer interactions affect consumer perceptions (e.g., [42]) but has not yet explored how observing brand-to-brand interactions do so. Furthermore, we contribute to research on the fundamental dimensions by which people judge other people and brands: warmth and competence (e.g., [ 1]). We identify brand-to-brand praise as a novel antecedent that often leads consumers to perceive brands that praise their competitors as warmer. We show that praising a competitor is viewed as a costly action that does not obviously benefit the praiser, thus making it a credible signal. In doing so, we demonstrate the role of costliness in signaling warmth that effectively combats consumer skepticism, a major barrier to warmth identified in prior research ([18]). We also introduce two moderators—organization type and individual differences in skepticism—to identify when costly displays of warmth are most important.Second, we further contribute to the literature on warmth and competence by introducing a context in which brand-to-brand praise increases warmth without damaging perceptions of competence. Prior work suggests that warmth and competence are often negatively correlated, particularly in contexts in which people are considering two or more entities ([32]). Leveraging consumers' lay theories about the characteristics of brands that would be willing to praise their competitors, brand-to-brand praise provides an opportunity for brands to communicate warmth while maintaining perceptions of competence.Third, while prior work has examined brand communication in which the competitive brand is ultimately shown to be inferior, such as in comparative advertising and two-sided messaging campaigns (e.g., [ 4]; [17]; [40]), our research identifies how directing attention away from one's own brand and toward the competition in a purely positive light affects brand evaluations and choice.Finally, we add to the current literature on praise by identifying brand-to-brand communication as a viable and distinct form of praise, noting that observers respond more favorably to praise in a brand-to-brand context than is typically observed in traditional person-to-person contexts. In doing so, we also highlight the understudied effects of praise in competitive relationships. In what follows, we review literature on brand relationships, praise, and brand warmth and present seven studies that test our hypotheses. Building Consumer–Brand RelationshipsA great deal of research has investigated how brands establish and build relationships with consumers, identifying influential factors such as the personalities, motives, and communication styles of both consumers and brands (for a review, see MacInnis, Park, and Priester [2009]). Surprisingly, researchers interested in brand relationships have not yet explored how brands' public communication with other brands affects their relationships with consumers. Of course, researchers have explored how strategic partnerships, such as brand collaborations and alliances, affect brand outcomes (e.g., [38]; [57]). However, these brand interactions occur largely behind the scenes and reflect formal business arrangements as opposed to informal, public communication that makes consumers privy to how a brand treats its competitors. We suggest that consumers will infer important information about a brand based on the way it communicates with other brands. Specifically, we posit that consumers who observe a brand praising its competition will perceive the praiser brand as having positive intentions toward others, known as brand warmth. Subsequently, consumers will develop more positive evaluations of and interest in the praiser brand. Brand-to-Brand Praise and Warmth PerceptionsPrior research has established that people judge others—individuals, groups, cultures, countries—using two fundamental dimensions, often referred to as warmth and competence ([25]; [32]).[ 5] Warmth is the degree to which one has positive intentions toward others and includes perceptions of thoughtfulness, kindness, honesty, and trustworthiness ([ 1]; [25]). Evolutionarily speaking, it allowed our ancestors to quickly distinguish friend from foe and prepare to fight or flee. Warmth judgments are therefore formed more quickly and generally have the greatest impact on attitudes toward individuals ([64]). Conversely, competence reflects the degree to which one is able to enact one's intentions.Given that people relate to brands similarly to how they relate to people in many ways ([26]), warmth and competence are important traits for firms to consider ([33]). Surprisingly, scant literature has explored the drivers of warmth and competence for brands ([ 1]). The research that does exist identifies factors such as a firm's profit focus ([ 2]), social responsibility ([ 8]), racial dynamics ([ 6]), and expression/communication style ([61]; [65]) as affecting consumers' perceptions of warmth and competence. Significantly more work has explored the consequences of warmth and competence. As examples, research has shown that warmth and competence perceptions affect consumer emotions ([ 1]), product evaluations and interest ([10]; [14]; [34]; [36]; [39]; [58]), and word of mouth ([ 7]; [54]). Importantly, while the precise contribution of warmth versus competence to different downstream consequences varies (e.g., [37]), brands generally aspire to be strong on both dimensions and occupy the coveted ""golden quadrant"" ([ 1]).In this research, we suggest that brand-to-brand praise affects consumers' reactions primarily through perceptions of warmth. Prior research has theorized that warmth is established through signals of cooperation (vs. competition) and actions that appear to serve others as opposed to the self (i.e., actions that are ""other-profitable""; [16]; [52]). We suggest that offering praise to a competitor provides a strong illustration of such cooperative, ""other-profitable"" activity. Consumers will therefore perceive a brand that praises competitive brands as warmer (relative to a brand that engages in other types of common brand messaging). This then leads to positive downstream consequences, such as increased purchases. Costliness as an Antidote to SkepticismIn hypothesizing the positive effect of brand-to-brand praise on warmth, it is important to note that high warmth is more difficult to establish and maintain than high competence, as people are often skeptical of others' motives. That is, warm behavior is often discounted, considered to be driven by ulterior motives and easier to fake than competence ([18]; [56]). Such skepticism has also been identified as an issue in research on praise more specifically. For example, in many brand-to-consumer exchanges, such as salesclerk-to-consumer interactions, praise generates suspicion from the consumer; consumers often suspect ulterior motives behind a salesclerk's compliment and perceive the salesclerk to be insincere when the praise occurs before a purchase ([11]; [44]). Further, observers who witness praise happening among others tend to be more skeptical of the praiser and do not react as positively as recipients of the praise ([13]; [29]; [60]). This then leads to the question: When and why might brand-to-brand praise surmount the skepticism associated with praise and other displays of warmth?We suggest that brand-to-brand praise operates uniquely from the aforementioned person-to-person and brand-to-consumer displays of warmth, particularly when the praise is directed toward competitors, due to its costliness. Consumers assume that complimenting a competitor is a costly action that does not directly benefit the complimenting brand. Research across disciplines suggests that the costliness of an act is the key component of whether the act is perceived as a meaningful signal of an underlying trait rather than an uninformative act motivated by devious or ulterior motives, also known as ""cheap talk"" (e.g., [59]; [67]). A common example from the natural world is the male peacock's tail. The costliness of this large and colorful tail, such as how it can handicap the bird's ability to escape from predators, is what makes it a credible signal of fitness to potential mates. Only those who truly possess the focal underlying trait would or could incur such costs. Linking this principle to the present context, only brands that are truly warm would incur the real or potential costs of praising the competition. Consumers should therefore not be as suspicious of brands that praise competitors as compared with brands that engage in less costly messaging. This costliness is why brand-to-brand praise, when directed toward competitors, is a strong signal of warmth, differentiating it from other common types of praise (e.g., salesclerk-to-consumer), surmounting consumer skepticism, and driving its positive influence on consumer attitudes and reactions. Effect on Competence PerceptionsAlthough the focus thus far has been on perceptions of warmth, one might wonder whether brands sacrifice competence when enhancing perceptions of warmth via brand-to-brand praise. This is a reasonable concern, as prior research suggests that in comparative contexts, for people and brands alike, when warmth (competence) is relatively high for a given entity, perceptions of that entity's competence (warmth) suffer (e.g., [ 2]; [28]; [36]; [54]). We suggest that praising a competitor offers brands unique advantages that allow them to maintain perceptions of competence in the face of increasing warmth. In particular, we suggest that consumers hold a lay intuition that a brand that is willing to praise its competitors must be fairly confident in its own abilities, which then allows consumers to maintain, if not increase, positive perceptions of its competence. This is reminiscent of the lay belief—confirmed by academic research—that people who are secure in their identity are the most willing to be kind and compliment others (e.g., [ 9]; [22]; [41]; [63]). Still, while competence may play a role in driving the effects of brand-to-brand praise, we do not expect it to be the primary driver of increased interest in the praiser brand, as perceptions of competence should be most strongly driven by cues of status versus the cues of cooperation that are focal in brand-to-brand praise ([24]). Summary of the Current ResearchIn summary, we argue that brand-to-brand praise often promotes positive brand evaluations and choice of the praiser. Specifically, we predict that consumers will evaluate a brand more favorably and show more interest in the brand when observing brand-to-brand praise compared with observing traditional self-focused messages or even other benevolent messages (H1). We theorize that this is because such praise increases perceptions of the praiser brand's warmth (H2). Moderators/Boundary Conditions CostlinessWe expect that this effect exists when the praise is costly (H3), such as when the brand is praising a competitor, so as to provide a meaningful signal of warmth to the consumer. We reason that the costly signal of warmth indicates to the consumer that the brand truly has positive intentions toward others, even when it is not in the best interest of the brand. As prior work suggests, such warmth leads consumers to be more interested in identifying with, using, and sharing the brand ([ 7]; [37]). For-profit versus nonprofit organizationsAs additional evidence for the role of warmth, we expect the effect of brand-to-brand praise to be stronger for brands that are typically associated with lower levels of warmth than those already endowed with high levels of warmth; brands with high levels of warmth, such as nonprofits, have little to gain from increasing warmth even further (and thus less to gain from brand-to-brand praise). This suggests that for-profit brands (which are lower in warmth than nonprofit brands; [ 2]) will benefit more from brand-to-brand praise (H4). Chronic consumer skepticismFinally, we posit that brand-to-brand praise allows brands to manage consumer skepticism often associated with displays of warmth. We introduce an important moderator to illustrate this point: individual differences in consumers' skepticism toward brand messaging. We predict that the effect of brand-to-brand communication will be strongest among individuals who are highly skeptical of brands. This is because the costliness associated with praising a competitor minimizes the extent to which persuasion knowledge concerns are activated among this group of consumers as compared with more traditional brand communication (H5). We summarize these predictions in the conceptual model in Figure 1 and discuss the boundary predictions further in the study introductions relevant to each.Graph: Figure 1. Conceptual model. Overview of StudiesWe first demonstrate the positive effects of brand-to-brand praise (vs. more traditional messages) across three field and lab studies involving real, consequential behaviors (Studies 1a, 1b, and 2; H1). Next, we show that this effect is specific to praise that is aimed toward a brand's competitor and thus deemed costly (Study 3; H3). We then explore warmth as the key mechanism, demonstrating that it mediates the effect of brand-to-brand praise (Study 4; H2) and that the effect is strongest for for-profit brands that have greater need to enhance perceptions of warmth (Study 5; H4). Finally, we demonstrate that brand-to-brand praise has the largest effect on individuals with high levels of skepticism toward traditional brand communication (Study 6; H5). Throughout these studies, we also test for alternative explanations, including that praise confers benefits due to novelty or authenticity, or that the effect is driven by negative reactions to self-promotion (i.e., bragging) rather than positive reactions to praise. Finally, we also examine the role of competence in several studies (Studies 4, 5, and 6). Evidence suggests that, while perceptions of brand warmth are the primary mechanism underlying this effect, brand-to-brand praise does not harm and can even boost perceptions of competence, which can influence desired outcomes.Together, these studies provide insight into when and why brand-to-brand praise is beneficial for brands. Notably, however, we demonstrate (in Studies 3, 5, and 6) and explain (in the ""General Discussion"" section) when and among whom brand-to-brand praise might not be as beneficial or might even be less beneficial. Study 1: The Effect of Brand-to-Brand PraiseStudies 1a and 1b provide initial causal evidence for our hypothesis by testing the effects of various types of brand messaging on advertisement click-through rate and brand choice in a field and lab study, respectively. We compare the effects of brand-to-brand praise with a traditional self-promotion message from the brand. In addition, in Study 1a, we utilize another type of control message in the form of an endorsement from another organization in the industry to show that praising a competitor enhances evaluations more than receiving an endorsement from others. Further, the endorsement condition serves as a separate point of comparison, ensuring that the hypothesized difference between the brand-to-brand praise condition and the self-promotion condition can be interpreted as a boost from brand-to-brand praise and not merely a negative reaction to self-promotion. Study 1a: Facebook Advertisement Click-Through Rate MethodParticipants and design. This preregistered study utilized a three-cell between-subjects design: Facebook users saw an advertisement on Facebook featuring self-promotion, an external endorsement, or brand-to-brand praise.[ 6]Procedure. We created a fictitious car wash brand called Precision Car Wash and launched three advertisements for the brand on Facebook. The self-promotion message stated, ""Precision Car Wash is proud to receive the Industry Best 2020 Award."" The external endorsement consisted of a message from a fictitious organization, The Industry Best 2020 Award Committee, announcing Precision Car Wash as the year's award recipient. Finally, the brand-to-brand praise ad consisted of a message from Precision Car Wash congratulating another fictitious car wash business, LikeNew Car Wash, on winning the Industry Best 2020 Award (for study stimuli, see Web Appendix B). Facebook users who saw the ad could click on the message to be taken to the Facebook page of Precision Car Wash for more information. We measured the number of impressions and clicks, which allowed us to compare click-through rates (clicks as a percent of impressions; CTRs), for each ad. We ran the ads over the course of five days and used Facebook's recommended advertising algorithm to reach 80% power in testing the ads for a final sample of 13,719 impressions.We also conducted two separate supplemental tests of these stimuli. First, we conducted a manipulation check for the ads, assigning participants to one of the three ad conditions and asking them to indicate the extent to which the brand was praising its competition (1 = ""definitely NOT praising the competition,"" and 7 = ""definitely praising the competition""; N = 105). Second, given variations in the text used to communicate the focal messages (e.g., number of words, fonts), we conducted a posttest to assess the ""graphic design"" (three items: quality, clarity, graphic appeal) of the assigned ad (1 = ""not done very well,"" and 7 = ""done very well""; α = .87; N = 105). These items allowed us to be sure that our focal praise condition was not (unintentionally) more aesthetically appealing and more likely to be clicked for that reason. ResultsManipulation check and aesthetic appeal. The separate manipulation check showed a significant difference across conditions (F( 2, 101) = 21.47, p < .001, ηp2  = .30). Those who viewed the brand-to-brand praise message perceived it to praise the competition (M = 4.82) more than the self-promotion (M = 1.96; p < .001) and external endorsement (M = 2.43; p < .001) ads. The self-promotion and external endorsement conditions did not differ (p = .30). This manipulation check was conducted for and confirmed the manipulations in each of the remaining studies (see Web Appendix C).In addition, the separate test designed to assess the aesthetic appeal of the different messages indicated that the focal praise ad was not advantaged aesthetically. We find a significant difference across conditions (F( 2, 102) = 5.50, p = .005, ηp2  = .10) such that those who viewed the brand-to-brand praise ad perceived it to be lower in aesthetic appeal (M = 3.83) than the self-promotion ad (M = 5.03; p = .001) and not statistically different from the external endorsement ad (M = 4.42; p = .11). The self-promotion and external endorsement conditions did not significantly differ (p = .10). However, because the praise ad was rated to be lower in aesthetic appeal, any positive benefits of the praise ad should not be a result of viewers liking the appearance of the praise ad more.CTR. A chi-squared analysis revealed that the CTR differed across conditions (χ2( 2, N = 13,719) = 91.59, p < .001). The percentage of those who clicked on the ad was greater for the brand-to-brand praise condition (5.4% of 4,392 impressions) compared with the self-promotion (3.3% of 4,075 impressions; χ2( 1, N = 8,467) = 21.42, p < .001) and external endorsement (1.8% of 5,252 impressions; χ2( 1, N = 9,644) = 90.45, p < .001) conditions. Study 1b: Lab Brand Choice MethodParticipants and design. One hundred fifty-four members of the local community (general public, students, and staff) were recruited through a business school's behavioral lab. Participants were randomly assigned to one of two conditions: self-promotion or praise.Procedure. First, participants learned that they would give their opinions about snack brands that the business school was considering for its café and vending machines. Next, they were informed that they would be able to choose a sample snack to take home after the study.Participants then saw two advertisements from real local popcorn shops in the Raleigh-Durham, North Carolina, area. The first ad was from the recipient brand, Carolina Popcorn Shoppe, and stated, ""Come check out our FIVE newest flavors! In-store or online."" Everyone saw this ad. The second ad was from the focal brand of the study, The Mad Popper, and differed by condition. The praise condition ad read, ""We love good popcorn. Big shout-out to Carolina Popcorn Shoppe on their FIVE new flavors!"" The self-promotion ad read, ""We love good popcorn. Come explore our FIVE brand new flavors! In-store or online"" (Web Appendix D).Next, participants were reminded that both brands were being considered for use at the business school. They were then asked to choose Carolina Popcorn Shoppe or The Mad Popper as the brand they would prefer to sample, which they received at the conclusion of the study.[ 7] Finally, participants responded to a stimuli believability measure, assessing their willingness to suspend disbelief and assume that the experimental stimuli were real (""How believable was this advertisement by The Mad Popper?""; 1 = ""not at all,"" and 7 = ""very""). We asked this believability question consistently across experiments (except Study 1a, where we could not) because of the low prominence of brand-to-brand praise currently in the market. Although brand-to-brand praise is not yet commonly practiced in the real world and thus may not yet be highly believable (i.e., seem real) for some consumers, we are examining what could happen if consumers witnessed brand-to-brand praise, meaning controlling for variation in whether the stimuli were believed to be real.[ 8] We use believability as a covariate across each subsequent experiment to enhance experimental power ([45]). Details on this measure appear in Web Appendix E, which includes a summary of key results with and without the covariate.[ 9] ResultsBrand choice. We conducted a binary logistic regression with condition (1 = praise, 0 = self-promotion) as the key predictor and believability as a continuous covariate on brand choice (1 = The Mad Popper, 0 = Carolina Popcorn Shoppe). Conceptually replicating the results of Study 1a, those in the praise condition were significantly more likely to choose the focal brand, The Mad Popper, compared with those in the self-promotion condition (β = .91, SE = .43, Wald χ2( 1, N = 154) = 4.47, p = .03). In percentages, 27.63% chose the focal brand in the self-promotion condition, and 34.62% did so in the praise condition.[10] We also find that believability does not act as a moderator (β = −.02, SE = .25, Wald χ2( 1, N = 154) = −.08, p = .93); thus, we use it as a covariate in our remaining studies (for moderation by believability results for the remaining studies, see Web Appendix E). DiscussionStudies 1a and 1b provide initial experimental evidence that brand-to-brand praise can have positive consequences for the praiser on real behavior, including advertisement CTR and brand choice. The comparison of brand-to-brand praise to an external endorsement in Study 1a suggests that the key effect is not because consumers dislike the other control condition—the self-promotion message—but is instead driven by a boost from observing brand-to-brand praise. Furthermore, by presenting participants with a forced choice between two brands in Study 1b, this data begins to suggest that praise benefits the praiser more than the praised. We further explore this in Study 2. Study 2: How Are Real Purchases Affected for the Praising and Praised Brands?In Study 2, we build on the findings of Studies 1a and 1b in assessing consumers' real, behavioral reactions to brand-to-brand praise, but we do so with some important changes. First, we assess a longer-term reaction to brand-to-brand praise by investigating purchase behavior for popular national brands (Kit Kat and Twix) 11 days after people were exposed to the brand messaging. Second, unlike Study 1b (but similar to Study 1a), we only expose participants to the competitor brand in the brand-to-brand praise condition. This enables us to further assess the potential costs of making competitive brands more top-of-mind (via praise) than they might otherwise be. This study also allows us to assess behavioral reactions to both the focal and competitive brands by gauging consumers' purchase behavior toward both, as opposed to forcing a choice between them (Study 1b) or tracking reactions to only the focal brand (Study 1a). Finally, this design offers an opportunity to see the proposed effect of praise on behaviors of greater financial consequence for the consumer and the brand: purchases. Methods Participants and designThis preregistered study had a two-cell between-subjects design (control, praise) and was conducted in two stages on Prolific.[11] First-stage procedureIn the first stage of this study, participants recruited from Prolific (N = 1,502; 49.6% female) viewed an image of Kit Kat's Twitter page. In the control condition, participants read a tweet that said, ""Start your day off with a tasty treat!"" In the praise condition, participants read a tweet that said, ""@twix, Competitor or not, congrats on your 54 years in business! Even we can admit—Twix are delicious"" (Web Appendix F). Unlike Study 1b (but similar to Study 1a), participants did not see a separate introduction to the nonfocal brand, Twix. Thus, Twix would presumably not be as top-of-mind unless they read the praise tweet. Afterward, we measured participants' attitudes toward both Kit Kat and Twix using the attitude measure from [46] (""negative/positive,"" ""dislikeable/likeable,"" and ""unfavorable/favorable"" on seven-point scales; α = .95; for details on this measure, see Web Appendix F). Second-stage procedureFrom the initial sample of 1,502 participants, we excluded those who indicated that they have not bought chocolate candy for themselves within the past six months, those with dietary restrictions that prevent them from buying chocolate candy, and those who indicated that they were not interested in completing a follow-up survey, leaving us with a sample of 1,298 potential participants for the second stage. Approximately 11 days after participants completed the first stage, we sent a follow-up survey to the 1,298 participants. Of these participants, 772 participants completed the second-stage survey. Attrition rates were similar across conditions (control = 40.64%, praise = 40.40%, χ2( 1, N = 1,298) = .008, p = .93).In the second-stage survey, participants indicated whether they had purchased any Kit Kats (yes/no) or Twix (yes/no) since they took the first portion of the survey. Finally, we asked participants in an open-ended question what they could recall about the tweet they saw in the first stage of the study, bringing the tweet to their attention so that we could measure the believability of the tweet using an adapted version of the measure in the prior study. ResultsWe conducted a binary logistic regression with condition (1 = praise, 0 = control) as the key predictor and believability as a continuous covariate on purchase behavior (1 = purchased Kit Kat, 0 = did not purchase Kit Kat). Those in the praise condition were significantly more likely to purchase Kit Kat compared with those in the control condition (β = .34, SE = .16, Wald χ2( 1, N = 772) = 4.17, p = .04). In raw percentages, 23.77% in the control condition purchased Kit Kat versus 31.95% in the praise condition. We conducted the same analysis for purchase behavior for Twix and find that there was no difference between the praise and control conditions (β = −.25, SE = .19, Wald χ2( 1, N = 772) = 1.68, p = .20) DiscussionStudy 2 extends our prior findings, demonstrating the effect of brand-to-brand praise on actual purchase behavior over a longer time horizon and relative to competitors' purchase outcomes. In doing so, we also show that even if the competitor brand becomes more salient than it would have normally been as a result of brand-to-brand praise, such praise still primarily provides positive benefits to the focal brand.Notably, in a supplemental study, we replicate the current findings using a paradigm with Subway and Jimmy John's (Web Appendix G). We find that when Subway brings Jimmy John's into the consideration set through brand-to-brand praise, Subway gains a boost in brand attitude compared with the self-promotion condition; Jimmy John's, however, does not gain a boost from receiving the praise.In the remaining studies, we identify when the beneficial effects of praise are mitigated and the underlying mechanism of the effect. In doing so, we offer additional understanding of the psychological processes driving consumer responses to praise as well as practical insights for choosing the appropriate recipients of and circumstances for praise. Study 3: The Effects of Brand-to-Brand Praise Toward Competitors Versus NoncompetitorsIn Study 3, we expand on the findings of the prior studies by examining the effects of brand-to-brand praise toward both a direct competitor and a noncompetitor. We expect that when a brand compliments a relevant direct competitor, which we define as a brand that competes in the same category as the focal brand, consumers view that compliment as relatively costly or risky because a brand has more to lose when bringing positive attention to such competition. It is such costliness that credibly signals that the brand must truly have warm intentions. We do not expect our effects to hold when a brand compliments an irrelevant noncompetitor, which we define as a brand that does not compete in the same category as the focal brand. In this scenario, the brand incurs lower cost by bringing attention to the irrelevant noncompetitor because there are less obvious repercussions, and thus the compliment is a less credible signal of a brand's warmth. This study also aims to demonstrate that the effects of brand-to-brand praise are not driven solely by the perceived novelty of the message or by negative reactions to self-promotional messages. Method Participants and designThree hundred ninety-nine participants (50.4% female) recruited from Amazon Mechanical Turk took part in this four-cell between-subjects design (helpful control, self-promotion, costly praise, noncostly praise). ProcedureWe created two eyeglasses brands for this study, Franklin's Frames and Lazlo's Lenses, and introduced them to participants as competitors. Participants then viewed an ostensibly recent series of three tweets from the focal brand, Franklin's Frames—one manipulated tweet that varied by condition and two filler tweets. The manipulated tweet in the praise condition read, ""@lazloslenses, Wow! Your new frames are looking good!"" with an image of a pair of glasses. In the self-promotion condition, the manipulated tweet read, ""Wow! Our new frames are looking good!"" with an image of a pair of glasses identical to the praise condition. In the helpful control condition, the manipulated tweet read, ""How to clean your frames:"" with a screen shot from a video showing how to clean eyeglasses. Participants in the noncostly praise condition saw a burger brand that is clearly not a direct competitor to Franklin's Frames, called Ben's Burgers. The focal tweet for those in the noncostly praise condition said, ""@bensburgers, Wow! Your new burgers are looking good!"" with an image of a burger attached (Web Appendix H).After reading the scenario, participants completed the measures of brand attitude (α = .95) noted in Study 2. In addition, we measured the perceived costliness of the tweets with four items on seven-point scales (α = .75; Web Appendix C). Finally, participants answered the same believability question as in prior studies as well as a measure of perceived novelty (""How novel was this tweet by Franklin's Frames?""; 1 = ""not novel,"" and 7 = ""very novel"") of the focal tweets. Results CostlinessA one-way analysis of covariance (ANCOVA) showed significant differences across conditions on perceived costliness of the focal tweet (F( 3, 394) = 37.66, p < .001, ηp2  = .22).[12] As we expected, contrasts revealed that perceived costliness was significantly greater for the costly praise condition (M = 3.82) than the self-promotion (M = 2.21, p < .001), control (M = 2.37, p < .001), and noncostly praise (M = 2.52, p < .001) conditions. Perceived costliness for the noncostly praise condition was greater than the self-promotion condition (p = .05) but not significantly different from the control condition (p = .34). Lastly, the self-promotion and control conditions did not differ (p = .32). Notably, we measure perceived costliness of the stimuli in all of our studies and find the same pattern of results; praise toward a direct competitor is perceived as more costly than other types of messages (Web Appendix C). Brand attitudeA one-way ANCOVA revealed an effect of condition on brand attitude toward the focal brand, Franklin's Frames (F( 3, 394) = 4.83, p = .003, ηp2  = .04). Contrasts revealed that brand attitude was significantly greater for the costly praise condition (M = 6.05) than the control (M = 5.43, p < .001) and self-promotion (M = 5.46, p < .001) conditions. Brand attitudes were also significantly greater for costly praise than noncostly praise (M = 5.65, p = .02). Brand attitude for those in the control, self-promotion, and noncostly praise conditions were not significantly different (all ps > .18), suggesting that the results are driven by a boost from the praise message and not a dislike of the self-promotion message. NoveltyUnsurprisingly, a one-way ANCOVA revealed significant differences across conditions on how novel the tweet seemed (F( 3, 394) = 8.80, p < .001, ηp2  = .06). Contrasts revealed that both the noncostly praise (M = 4.30) and the costly praise (M = 4.56) conditions were perceived to be more novel than the control (M = 3.48) and self-promotion (M = 3.43, all ps ≤ .001) conditions. Crucially, however, there was no difference in perceived novelty between the noncostly praise and the costly praise conditions (p = .31), suggesting that the differences between these conditions on brand attitude were not driven purely by differences in novelty.[13] DiscussionIn Study 3, we replicate the findings of prior studies, demonstrating that brand-to-brand praise between competitors boosts brand evaluations compared with other messages, including self-promotion and a helpful control message. Furthermore, we find that noncostly praise (i.e., praise toward irrelevant noncompetitors) did not give the same boost. This result suggests that simply speaking positively about another brand is not enough; the consumer must view the compliment as costly to the brand for it to have positive effects. Although we find costliness to be necessary to show the benefits of praise, it is not the underlying driver of the effects (mediation comparing the self-promotion and costly praise conditions: ab = .06, 95% confidence interval [CI] = [−.045,.191]; PROCESS Model 4, 5,000 bootstrap samples; [31]). This study also begins to cast doubt on novelty as a primary driver of the effect, given that the noncostly praise was perceived to be as novel (but not evaluated as positively) as the costly praise. We further test the role of novelty in subsequent studies. In addition, we again find that the effects on brand attitude do not occur because consumers dislike self-promotion messages but are instead driven by the boost from seeing the costly praise message, replicating the findings of Studies 1a and 2. Study 4: Testing Warmth as the MechanismTo begin to understand why brand-to-brand praise increases brand evaluations and choice, we conducted a qualitative pretest gauging people's natural reactions (i.e., inferences, attributions) to observing a brand complimenting a competitor. Participants (N = 150) on Prolific saw a tweet from PlayStation congratulating Nintendo on its Nintendo Switch launch and then listed five adjectives to describe PlayStation. We find that observers more frequently attribute warmth-related words (e.g., friendly, supportive, kind; 55.73% of the adjectives), rather than competence-related words (e.g., confident, successful, intelligent; 17.07% of the adjectives), or any other kind of perception, to the brand, implicating warmth as the most top-of-mind inference following brand-to-brand praise. For additional insight, we asked participants to take the perspective of a manager and indicate why they would or would not post a message praising the competition. We find that most participants would be willing to praise a competitor (69.33%). Again, the majority of the responses (61.5%) indicated warmth to be the primary reason for the action (e.g., ""I want to show others that I act in selfless ways""). Moreover, some responses also positively noted competence-related reasons (e.g., ""have confidence in my own brand and its qualities""; ""we appear to be positive and not insecure about our place in the market"") either exclusively or in combination with warmth (25%), suggesting that while warmth-related reasons are more top-of-mind, perceptions of competence are unlikely to be harmed (for pretest details, see Web Appendix I).In light of these initial qualitative insights, we empirically test warmth as the driver of the benefits of brand-to-brand praise and compare it with competence in Study 4. We predict that brand-to-brand praise enhances perceptions of brand warmth, which predominantly drives the boost in brand evaluations, rather than competence. Ultimately, we expect the heightened brand evaluations to drive consumer action, which is measured in this study as their willingness to sacrifice their own time (for free) on behalf of the brand. Methods Participants and designTwo hundred participants (57% female) from Prolific took part in this two-cell between-subjects study (control, praise). ProcedureParticipants were introduced to a real tea brand, Treecup Tea, from Kickstarter. They were told that Treecup Tea focuses on making high-quality tea blends and competes with other tea companies on Kickstarter for funds. Participants read that Treecup Tea's competitors include Teafir, Shisso Tea, and Phat Tea. Next, participants were introduced to Treecup Tea's Twitter page. For the control condition, participants saw only the brand's Twitter header. We use this control condition, different from the self-promotion control in prior studies, to show again that the boost for the brand results from people feeling positive about the praise message instead of negative toward self-promotion. For the praise condition, participants saw a Twitter page that consisted of three praise messages toward the three competitors interspersed throughout other tweets on the page (e.g., ""@ShissoTea Your tea is fresh and sustainable! That's amazing!""; Web Appendix J).Participants then completed the same measure of brand attitude as in prior studies. Participants were also asked how much time (scale from 0 to 4 minutes, in 30-second increments) they were willing to volunteer to answer some market research questions for Treecup Tea after the main study, without additional pay. We chose this dependent variable as an action of consequence to the consumer, given that volunteering time is a costly consumer behavior. Finally, participants completed measures for warmth (""warm, friendly"" on a seven-point scale; r = .81) and competence (""competent, capable"" on a seven-point scale; r = .90; [ 1]). The order of the warmth and competence measures was randomized to ensure that one did not explain the effect more than the other simply due to order. We again measured believability of the stimuli as a covariate in our analyses. Results Brand attitudeReplicating prior results, a one-way ANCOVA revealed a significant effect of message (F( 1, 197) = 17.61, p < .001, ηp2  = .08), whereby praise (M = 5.86) led to greater brand attitude compared with the control (M = 5.33). Volunteer timeThe data were skewed because 34.1% of the sample indicated that they would not volunteer any time,[14] so we conducted a binary split on volunteer time (1 = those who would volunteer any amount of time, 0 = those who would not volunteer). A binary logistic regression with condition (1 = praise, 0 = self-promotion) and believability as the predictors on willingness to volunteer revealed that those in the praise condition were significantly more likely to volunteer time compared with those in the self-promotion condition (β = .59, SE = .30, Wald χ2( 1, N = 200) = 3.76, p = .05).[15] Warmth and competenceA one-way ANCOVA revealed a significant effect of message on warmth (F( 1, 197) = 22.54, p < .001, ηp2  = .10): praise (M = 5.92) led to greater perceptions of warmth compared with the control (M = 5.20). A one-way ANCOVA also revealed a smaller but significant effect of message on competence (F( 1, 197) = 5.59, p = .02, ηp2  = .03): praise (M = 5.49) led to greater perceptions of competence compared with the control (M = 5.17). MediationNext, we tested the effect of praise on willingness to volunteer through warmth and brand attitude as serial mediators. We find that the praise message leads to increased warmth perceptions, which leads to improved brand attitude and, subsequently, greater willingness to volunteer (indirect = .08, 95% CI = [.008,.228]). However, a serial mediation model replacing warmth with competence is not significant (indirect = .05, 95% CI = [−.005,.169]). DiscussionIn Study 4, we replicate prior findings and also demonstrate a novel downstream consequence of praise whereby observers are more likely to give up some of their time, without additional compensation, to help the praiser brand after viewing a praise message. We find warmth to be the most direct driver of the effects of brand-to-brand praise. However, given that competence perceptions also benefit from a praise message, it is worth considering how brand-to-brand praise may usher brands into the ""golden quadrant"" ([32]) of warmth and competence dimensions. Lastly, the stimuli used for the praise message in this study consisted of three compliments to three different competitors on a single Twitter page. The fact that we still see a boost in brand attitude suggests that giving praise repeatedly, at least to some extent, may not harm the brand in this context (an idea we revisit in the ""General Discussion"" section). Study 5: Moderated Mediation by Organization TypeIn Study 5, we again test for warmth as the mechanism underlying the effect of brand-to-brand praise on evaluations, and we do so with process by moderation, directly manipulating (and again also measuring) brand warmth. Compared with brands that are lower in warmth, we predict that brand-to-brand praise will not increase brand evaluations as much for brands that are higher in warmth. We theorize that this is because brands that are already high in perceived warmth will not have as much need or space to grow in that aspect, thus rendering praise less influential. In contrast, brands with lower perceived warmth at baseline will benefit more from the boost given by brand-to-brand praise. Based on prior research demonstrating that nonprofit organizations are high in perceived warmth ([ 2]), we compare the effects of praise on nonprofit and for-profit organizations. Beyond its relation to our theory, comparing the effect of praise in for-profit versus nonprofit organizations is of practical importance, as it will help managers from these clearly identifiable sectors better assess how effective brand-to-brand praise may be for their brands. Lastly, we measure perceptions of the brand's arrogance as an alternative explanation, given that self-promotional messages may be perceived as a form of bragging. We also measure perceptions of authenticity and novelty of the message as other potential explanations for the effect. Method Participants and designSix hundred one participants (62.2% female) from Prolific took part in this 2 (message: control, praise) × 2 (organization type: nonprofit, for-profit) between-subjects experimental study that was preregistered.[16] ProcedureParticipants were introduced to a fictitious internet service organization, Tech Dev. In the nonprofit condition, participants were told that Tech Dev was a nonprofit organization that had a goal of helping the community. In the for-profit condition, participants read that Tech Dev was a for-profit organization with a goal of increasing profits. Participants were also told that Tech Dev competed with another organization, Networks.org or Networks.com, for either donations or sales (depending on its nonprofit or for-profit status, respectively). Next, participants were shown the Twitter page of Tech Dev in which they read either a control message (""Want to know more about the quality of our services? Click here: techdev[.com/.org]/internet"") or a message praising Networks (""Networks[.com/.org], we are impressed by the quality of your services—competitor or not!""; Web Appendix K).Participants then indicated their interest in Tech Dev using two measures (""How likely are you to seek out more information about Tech Dev?"" and ""How willing are you to talk to a customer representative to learn more about Tech Dev?""; r = .79).[17] We also measured warmth, competence, and believability of the stimuli. As in the prior study, the order of warmth and competence was randomized. In addition, we measured the extent to which participants' assigned condition was perceived as arrogant (braggy, conceited, arrogant; α = .94), authentic (authentic, self-aware; r = .65), and novel (single-item measure) as alternative explanations,. Results Brand interestUsing a two-way ANCOVA, we find a main effect of message (F( 1, 596) = 24.51, p < .001, ηp2  = .04), a main effect of organization type (F( 1, 596) = 35.82, p < .001, ηp2  = .06), and a significant interaction (F( 1, 596) = 4.79, p = .03, ηp2  = .008). As predicted, in the for-profit conditions, we replicate prior results in that praise (M = 3.74) led to greater brand attitude than the control did (M = 2.78; p < .001). In the nonprofit conditions, praise (M = 4.25) also led to greater brand evaluations than the control did (M = 3.86; p = .04), but to a diminished extent. WarmthWe find a similar pattern for warmth. A two-way ANCOVA revealed a main effect of message (F( 1, 596) = 98.85, p < .001, ηp2  = .14), a main effect of organization type (F( 1, 596) = 124.43, p < .001, ηp2  = .17), and a significant interaction (F( 1, 596) = 8.44, p = .004, ηp2  = .01). In the for-profit conditions, we again replicate prior results where praise (M = 4.78) led to significantly greater warmth compared with the control (M = 3.37; p < .001). In the nonprofit conditions, we find that praise (M = 5.67) led to weaker, though still significant, effects compared with the control (M = 4.88; p < .001). CompetenceWe also find a similar pattern for competence. A two-way ANCOVA revealed a main effect of message (F( 1, 596) = 26.90, p < .001, ηp2  = .04), a main effect of organization type (F( 1, 596) = 23.33, p < .001, ηp2  = .04), and a significant interaction (F( 1, 596) = 4.49, p = .03, ηp2  = .007). In the for-profit conditions, we find that praise (M = 5.22) led to greater competence compared with the control (M = 4.53; p < .001). In the nonprofit conditions, we find that praise (M = 5.48) led to weaker, though still significant, effects compared with the control (M = 5.18; p = .02). AlternativesWe do not find the same pattern of results for bragging. A two-way ANCOVA revealed only a main effect of organization (F( 1, 596) = 55.77, p < .001, ηp2  = .09) such that the nonprofit (M = 2.08) was perceived as less arrogant than the for-profit (M = 2.93).For authenticity, we find a similar pattern to that of warmth in which there was a significant interaction (F( 1, 596) = 16.33, p < .001, ηp2  = .03) where the boost from praise was stronger in the for-profit conditions. We find similar patterns for novelty (interaction F( 1, 596) = 6.58, p = .01, ηp2  = .01). Moderated mediationWe first tested the predicted model. Specifically, we tested for moderated mediation (PROCESS Model 7, 5000 bootstrap samples, [31]) for the effect of praise on brand attitude through warmth, moderated by organization type, controlling for believability. As expected, we find that organization type significantly moderated the mediation through warmth (index of moderated mediation = .36, 95% CI = [.119,.609]), and the mediation effect is stronger in the for-profit conditions (ab = .82, 95% CI = [.624, 1.039]) compared with the nonprofit conditions (ab = .46, 95% CI = [.299,.632]).Then, to explore the role of alternative explanations, we entered all the potential mediators (warmth, competence, bragging, authenticity, and novelty) in parallel into the moderated mediation model. We find that warmth remains a significant mediator in the model (index of moderated mediation = .23, 95% CI = [.079,.408]), suggesting that none of these alternatives ""swamp"" warmth in explaining this effect. Next, we find that the indices of moderated mediation for competence (index of moderated mediation = .08, 95% CI = [.005,.180]), authenticity (index of moderated mediation = .13, 95% CI = [.022,.271]), and novelty (index of moderated mediation = .12, 95% CI = [.025,.231]) were also significant. However, the index for bragging was not significant (index of moderated mediation = −.02, 95% CI = [−.083,.038]). Although competence, authenticity, and novelty were also significant mediators in the model in addition to warmth, warmth remains significant with the largest index of moderated mediation.Because warmth is most frequently compared with competence, we conducted a final analysis statistically comparing the sizes of their indirect effects in a parallel mediation model. We do this within the for-profit conditions in which the effects were more prominent (and because we are unable to statistically compare the full moderated mediation models). We find that the indirect effect for warmth as a mediator was significantly greater than that of competence (p < .001, 95% CI = [.205,.718]). Thus, though other factors may play a role, our results point to warmth as the primary driver of the effects. DiscussionStudy 5 sheds further light on warmth as the primary mechanism underlying brand-to-brand praise. We demonstrate that brand-to-brand praise increases perceived warmth, which enhances brand interest, but this effect is attenuated when the brand is already high in perceived warmth (e.g., nonprofits), as there is less room and need for growth in warmth. Thus, the benefits of praise can be most clearly seen among brands that are perceived as less warm (e.g., for-profits) at baseline. In addition, while perceptions of competence, authenticity, and novelty may play a role in driving brand interest, we show warmth to be the more consistent, primary driver of the effect. Finally, we rule out bragging as an alternative explanation. Study 6: Moderation by SkepticismIn Study 6, we look to another context in which the effects of brand-to-brand praise may be attenuated. Here, we examine skepticism toward advertising as an individual difference that may moderate the effects of praise. Advertising skepticism is an individual difference that has been defined as consumers' chronic doubt or mistrust in a marketer's message ([47]). Individuals who are more skeptical are generally less persuaded by marketing messages and are less trusting of brands. As brands are confronting an ""age of cynicism"" where skepticism is at an all-time high ([21]; [49]) and 71% of consumers report having little faith in brands ([30]), it is very important to understand its effects.As seen in the previous study, the effects of brand-to-brand praise become more prominent when there is room for increasing perceptions of brand warmth. Consumers who are more skeptical of advertising distrust that brands have positive intentions, or warmth, and thus inherently provide brands with greater room to improve in warmth than their nonskeptical counterparts who are already trusting. Thus, brand-to-brand communication may be most effective among skeptics, as it can bypass their cynicism and boost perceptions of warmth. Moreover, such skeptics are generally more persuaded by nonadvertising sources of information and emotional appeals ([48]), which allow marketers to circumvent consumer resistance by decreasing the activation of persuasion knowledge ([27]). Because brand-to-brand praise does not explicitly aim to promote one's brand and instead relies on the more emotional signal that the brand is high in warmth, it is less likely to activate persuasion knowledge and more likely to be accepted. Thus, we predict that consumers high in skepticism will be the most affected by brand-to-brand praise. Study 6 tests this idea utilizing two well-known competitors, Lyft and Uber. Method Participants and designSix hundred participants were recruited from Prolific for this 2 (message: self-promotion, praise) × measured (advertising skepticism) preregistered study.[18] ProcedureParticipants first completed the skepticism toward advertising scale ([47]; 1 = ""strongly disagree,"" and 5 = ""strongly agree"") and then completed filler items. Next, participants saw Lyft's Twitter page, where they read either a self-promotion tweet (""Congratulations to us on all our achievements this past year!"") or a praise tweet toward Uber (""@Uber Congratulations on all your achievements this past year!""; Web Appendix L). Then, participants completed the same measure of brand attitude, warmth, and competence as in previous studies. We also measured perceptions of bragging, authenticity, and novelty using the same measures as in Study 5 as potential alternative explanations, as well as believability of the stimuli as a covariate. Results Brand attitudeReplicating prior results, an ANCOVA controlling for believability revealed a main effect of message on brand attitude (F( 1, 597) = 22.12, p < .001, ηp2  = .04), where praise (M = 5.22) outperformed self-promotion (M = 4.74). Next, we find a significant interaction between message and skepticism (skepticism scale reverse coded; β = .21, SE = .10, p = .03; Figure 2) on brand attitude, such that when skepticism toward advertising was higher, the praise message led to a greater increase in brand attitude (Johnson–Neyman point = 1.48, 75.67% of participants; β = .23, SE = .12, p = .05). The effect of message was attenuated at lower levels of skepticism below the Johnson–Neyman point.Graph: Figure 2. Study 6: Moderation by skepticism. MediationWe find that praise significantly boosted perceptions of warmth (Mpraise = 5.36 vs. Mself = 4.99; F( 1, 597) = 13.76, p < .001, ηp2  = .02), competence (Mpraise = 5.52 vs. Mself = 5.32; F( 1, 597) = 4.88, p = .03, ηp2  = .008), authenticity (Mpraise = 4.90 vs. Mself = 4.56; F( 1, 597) = 9.46, p = .002, ηp2  = .02), and novelty (Mpraise = 4.58 vs. Mself = 4.28; F( 1, 597) = 6.59, p = .01, ηp2  = .01) compared with self-promotion, controlling for believability. Self-promotion was seen as more arrogant than praise (Mpraise = 3.18 vs. Mself = 4.16; F( 1, 597) = 54.23, p < .001, ηp2  = .08).We then tested for the predicted moderated mediation (PROCESS Model 7, 5000 bootstrap samples, [31]) for the effect of praise on brand attitude through warmth, moderated by skepticism, controlling for believability. While the index of moderated mediation was not significant (index = .10, 95% CI = [−.046,.247]), we find that the indirect effect patterns were in line with our predictions, such that the indirect effect is stronger and significant for those higher in skepticism (+1 SD = 3.17, ab = .31, 95% CI = [.084,.550]) and nonsignificant for those lower in skepticism (−1 SD = 1.30, ab = .12, 95% CI = [−.026,.274]).Next, we test for mediation with all of the potential alternative explanations in parallel (PROCESS Model 4, 5,000 bootstrap samples, [31]). Warmth (ab = .14, 95% CI = [.064,.230]), competence (ab = .04, 95% CI = [.004,.091]), bragging (ab = .09, 95% CI = [.049,.146]), authenticity (ab = .06, 95% CI = [.021,.116]), and novelty (ab = .03, 95% CI = [.004,.054]) mediate the effect of message on brand attitude. However, we again find that the indirect effect for warmth was the largest, pointing to its primary role in causing this effect.Finally, we statistically compared the indirect effects of warmth and competence in the model and find the indirect effect of warmth to be significantly greater than that of competence (p = .001, 95% CI = [.085,.349]), again suggesting that warmth plays the primary role in driving the effect of brand-to-brand praise on brand attitudes. DiscussionIn Study 6 (and in a behavioral replication in Web Appendix M),[19] we show that brand-to-brand praise can actually operate more effectively for people who are generally more skeptical of advertising, as brand-to-brand praise bypasses their suspicions and creates more favorable consequences for the brand. Further, while we find that competence, bragging, authenticity, and novelty play a role in driving the effects of brand-to-brand praise on brand evaluations, we still identify warmth as the primary, more consistent underlying driver. General DiscussionWe investigate how observing brand-to-brand praise affects consumers' brand evaluations and choices. Across a variety of different modes of communication (social media, print advertising, digital advertising), study methods (web scraping; field, lab, and online studies), contexts (consumer products and services), outcomes (brand attitudes, social media and advertising engagement, brand choice, purchase behavior), and praise content, we show that consumers who witness brand-to-brand praise between competitors form more favorable evaluations of the praiser brand than consumers who witness other forms of communication, including typical self-promotion messages, helpful messages, basic brand information, and even outside-industry praise (for a summary of contexts, conditions, and findings across studies, see Web Appendix N). In addition to showing robustness to a variety of praise messages in the presented studies, a preregistered supplemental study demonstrates that the effect is further robust to both general and specific praise (Web Appendix O). Furthermore, the varied study stimuli suggest that this effect is robust to brands in a wide range of industries—including car care, snacks, candy, eyewear, beverages, technology, and transportation—with competitive relationships varying in intensity. In other words, brand-to-brand praise seems to benefit the praiser in less competitively intense relationships (e.g., mom-and-pop popcorn brands) as well as more competitively intense relationships (e.g., PlayStation and Nintendo, Uber and Lyft) in a variety of industries. We trace this effect primarily to the notion that brand-to-brand praise signals a brand's warmth, which leads to improved brand evaluations and affects consumer choices.In addition, we show that this effect only exists when the praise is deemed to be associated with significant cost or risk (Study 3). Importantly, we find that the effects of brand-to-brand praise are diminished in some situations or among some consumers, such as when the brand is already high in warmth (Study 5) or among consumers who are already trusting of brand intent (Study 6). These boundaries provide further evidence for the crucial role that perceptions of warmth play in driving the benefits of praise. While other mechanisms may also play a role, as consumer behaviors are generally multiply determined ([35]; [55]), we find warmth to be the most consistent, primary driver of the effects of brand-to-brand praise. Theoretical ContributionsOur research makes several theoretical contributions to literature streams on brand perceptions and relationships, brand communication, and praise. First, we contribute to the brand perception literature by showing that consumers' perceptions of brands can be affected by viewing a brand's interactions with other brands. We demonstrate that observing brand-to-brand praise can positively affect perceptions of a brand's warmth and influence subsequent brand evaluations. Second, we add to the warmth and competence literature by introducing a novel context in which brand-to-brand praise increases warmth without harming perceptions of competence. Third, our research demonstrates that directing positive attention toward the competition instead of toward one's own brand brings about benefits for the praiser brand, unlike what brands would typically do in comparative advertising and two-sided messaging campaigns (e.g., [ 4]; [17]; [40]). Finally, we contribute to the literature on praise by identifying brand-to-brand communication between competitors as a feasible form of praise that is less likely to induce suspicion compared with praise in traditional person-to-person contexts. Marketing Implications and Future ResearchAs we have noted, our findings may be surprising to practitioners who have been regularly and reasonably advised to avoid bringing positive attention to their competitors. However, our studies show that in some circumstances, praise is a method of brand-to-brand interaction that can result in beneficial consequences for the praising brand. Managers might consider offering compliments to competitors to boost their own brand evaluations. In other words, brands can expand from solely focusing on brand-to-consumer relationships to also focusing on their brand-to-brand relationships. This is akin to the positive reactions that politicians sometimes receive when positively acknowledging their opponent ([12]). We suggest a new context in which positive acknowledgment can benefit brands. With the rise of the digital age, brands can easily ""speak"" with each other and be observed by consumers. While it is not uncommon for brands to speak via ""feuds"" on social media, such as when Wendy's teases McDonald's for using frozen beef ([19]), we show in a supplemental study (Web Appendix P) that positive communication provides unique advantages. Negative or snarky communication, directed at a competitor or even directed at the self (as in the case of two-sided messaging), does not provide the same increase in brand evaluations. Although there will likely be variation depending on the exact content and cleverness of snarky communication (a ripe area for future research), marketers would be wise to consider opportunities for brand-to-brand praise instead, perhaps utilizing social media as a platform, to foster a warmer image.Brand-to-brand praise may also be a valuable way to respond to competitors' actions. While the norm for companies responding to a competitor's new product release is to avoid saying anything that would bring attention to that competitor, our studies suggest that responding positively can increase purchases for the praiser brand without boosting the competitor to the same degree. Importantly, while prior work has shown that a brand's positioning as an underdog or market leader has important implications ([50]), brand-to-brand praise may be appropriate regardless of the competitor's market status. In a preregistered supplemental study (Web Appendix Q), we find that the favorable effects of brand-to-brand praise on brand evaluations are robust to the market leadership status of the brand. Future research could further explore how characteristics about the firm such as market leadership or firm size may factor into the effect of brand-to-brand praise.Similarly, while the variety of brands leveraged in our studies suggest that brand-to-brand praise is likely beneficial when directed toward both less intense (e.g., local popcorn brands) and more intense (e.g., PlayStation and Nintendo, Uber and Lyft) competitors, future research should more systematically explore the role of competitive intensity. Such research might explore brand-to-brand praise with indirect versus direct competitors, which would likely vary in levels of perceived costliness. Indirect competitors may include other brands that are in a similar industry but do not compete directly in the same product category, such as in the case of soda and water in the drinks industry. In Study 3, we find that praise toward a direct competitor is deemed to be costly and results in better brand evaluations compared with praise toward noncompetitors. However, would praise toward an indirect competitor be perceived to be costly enough to increase evaluations? Outside of direct competition, what else determines whether praise is deemed to be costly or not? How would consumers react if complementary brands, such as soda and popcorn brands, praised each other? While we suggest that praise should benefit the praiser as long as it is perceived as costly, understanding what kind of brand-to-brand praise is considered costly may be beneficial to marketers.In addition, future research could also explore how brand-to-brand praise compares to other types of messages that convey warmth. For example, brands often communicate prosocial messages, such as a food brand showing active support for a local food bank. Brands might also post self-deprecating messages that recognize their own room for improvement. While these types of messages may signal warmth, they may not benefit the messenger brand in the same way as brand-to-brand praise does because of the unique aspect of costliness associated with praising a direct competitor. Future research could compare these various types of brand messaging to find ones that benefit brands the most.Future work might also examine whether certain brand personalities benefit more from praise than others. Our for-profit versus nonprofit results in Study 5 hint that brands with personalities that are inherently associated with warmth (e.g., sincere brands) may benefit less. Relatedly, are there circumstances under which snarky interactions or backhanded compliments are better suited for some brands, such as Wendy's ([15])? We noted that negativity did not fare as well as positivity (Web Appendix P), but under what conditions might this be different? Cultural differences, such as collectivism and individualism, may also play an important role. Collectivists, known for valuing community and kinship, may be particularly likely to value the warmth associated with brand-to-brand praise ([ 3]). Furthermore, future work could also explore when the effects of brand-to-brand praise are affected by gender. While women may value warmth more in some circumstances ([66]), we do not find consistent moderation by gender in our studies (Web Appendix R), which is consistent with prior work showing that gender does not always moderate brand warmth perceptions ([ 5]). Further research is needed to better understand the possible role that factors such as brand personality, consumer culture, and gender play in affecting brand-to-brand praise responses.Another direction for future research involves investigating the optimal frequency of brand-to-brand praise. Although we demonstrate that some repetition may be acceptable (Study 4), one limitation of this research is the focus on a single instance of praise in a short span of time. Praise when repeated over time may become less effective or even backfire. Excessive praise may trigger suspicion in observers (e.g., [23]), and previous research has shown that suspicious praise can lead to the praiser being perceived as less sincere ([11]; [44]). Thus, future research might explore the optimal levels or repetition of praise and the consequences of excessive praise to prevent such a backfiring effect.To further prevent backfiring effects, future research might also explore what happens if the praised competitor leverages the praise ""against"" the praiser? For instance, what if a praised brand publicly suggests that they are so great that even their competition praises them? It is possible that, if used by the competitor in this manner, the praise could damage the praiser brand. However, it is also possible that this kind of act could be perceived as arrogant or manipulative, damaging the praised brand and eliciting sympathy for the praiser.Overall, this work raises the question: Are brands missing an opportunity to build positive relationships with consumers by not (publicly) building positive relationships with competitors? We suggest that marketers would be wise to explore the intriguing benefits of brand-to-brand praise. " 6,Blame the Bot: Anthropomorphism and Anger in Customer–Chatbot Interactions," Chatbots have become common in digital customer service contexts across many industries. While many companies choose to humanize their customer service chatbots (e.g., giving them names and avatars), little is known about how anthropomorphism influences customer responses to chatbots in service settings. Across five studies, including an analysis of a large real-world data set from an international telecommunications company and four experiments, the authors find that when customers enter a chatbot-led service interaction in an angry emotional state, chatbot anthropomorphism has a negative effect on customer satisfaction, overall firm evaluation, and subsequent purchase intentions. However, this is not the case for customers in nonangry emotional states. The authors uncover the underlying mechanism driving this negative effect (expectancy violations caused by inflated pre-encounter expectations of chatbot efficacy) and offer practical implications for managers. These findings suggest that it is important to both carefully design chatbots and consider the emotional context in which they are used, particularly in customer service interactions that involve resolving problems or handling complaints.","The use of artificial intelligence (AI) in marketing is on the rise, as managers experiment with the use of AI-driven tools to augment customer experiences. One relatively early use of AI in marketing has been the deployment of digital conversational agents, commonly called chatbots. Chatbots ""converse"" with customers, through either voice or text, to address a variety of customer needs. Chatbots are increasingly replacing human service agents on websites, social media, and messaging services. In fact, the market for chatbots and related technologies is forecasted to exceed $1.34 billion by 2024 ([71]).While some industry commentators suggest that chatbots will improve customer service while simultaneously reducing costs ([16]), others believe they will undermine customer service and negatively impact firms ([34]). Thus, while customer service chatbots have the potential to deliver greater efficiency for firms, whether—and how—to best design and deploy chatbots remains an open question. The current research begins to address this issue by exploring conditions under which customer service chatbots negatively impact key marketing outcomes. While many factors may influence customers' interactions with chatbots, we focus on the interplay between two common features of the customer service chatbot experience.The first feature relates to the design of the chatbot itself: chatbot anthropomorphism. This is the extent to which the chatbot is endowed with humanlike qualities such as a name or avatar. Currently, the prevailing logic in practice is to make chatbots appear more humanlike ([ 9]) and for them to mimic the nature of human-to-human conversations ([44]). However, anthropomorphic design in other contexts (e.g., branding, product design) does not always produce beneficial outcomes (e.g., [36]; [38]). Accordingly, we examine circumstances under which anthropomorphism of customer service chatbots may be harmful for firms.The second dimension explored in this research is a commonly occurring feature in customer service interactions, irrespective of the modality: customer anger. Anger is one of the most prevalent specific emotions occurring in customer service contexts; estimates suggest that as many as 20% of call center interactions involve hostile, angry, complaining customers ([24]). Furthermore, the prevalence of anger increased during the COVID-19 pandemic ([57]; [60]), so a higher proportion of interactions are likely to be with angry customers. Thus, it is both practically relevant to consider how customer anger interacts with chatbot anthropomorphism and theoretically relevant due to the specific responses (e.g., aggression, holding others accountable) evoked by anger that impact the efficacy of more humanlike technology.Across five studies including the analysis of a large real-world data set from an international telecommunications company and four experiments, we find that when customers in an angry emotional state encounter a chatbot-led service interaction, chatbot anthropomorphism has a negative effect on customers' satisfaction with the service encounter, their overall evaluation of the firm, and their subsequent purchase intentions. However, this is not the case for customers in nonangry emotional states. The negative effect is driven by an expectancy violation; specifically, anthropomorphism inflates preinteraction expectations of chatbot efficacy, and those expectations are disconfirmed. Our findings suggest that it is important to both carefully design chatbots and consider the emotional context in which they are used, particularly in common types of customer service interactions that involve handling problems or complaints. This research contributes to the nascent literature on chatbots in customer service and has managerial implications both for how chatbots should be designed and for context-related deployment considerations. Conceptual Framework Anthropomorphism in MarketingDeliberate marketing efforts have made anthropomorphism, or the attribution of humanlike properties, characteristics, or mental states to nonhuman agents and objects ([20]; [68]), especially pervasive in the modern marketplace. Product designers and brand managers often encourage customers to view their products and brands as humanlike, through a product's visual features (e.g., face-like car grilles; [40]) or brand mascots (e.g., the Pillsbury Doughboy; [66]). In digital settings, advances in machine learning and AI have ushered in a new wave of highly anthropomorphic devices, from humanlike self-driving cars ([70]) to voice-activated virtual assistants with human names and speech patterns (e.g., Amazon's Alexa; [32]).Extant research generally suggests that inducing anthropomorphic thought is linked to improved outcomes. ""Humanized"" products and brands are more likely to achieve long-term business success because they encourage a more personal consumer–brand relationship ([ 1]; [66]). Anthropomorphic product features can make products more valuable ([28]) and can boost overall product evaluations in categories, including automobiles, mobile phones, and beverages ([ 1]; [39]; [40]). [67] found that anthropomorphized products increased consumers' preference and subsequent choice of those products.Anthropomorphism of technology has also been shown to improve marketing outcomes. Humanlike interfaces can increase customer trust in technology by increasing perceived competence ([ 5]; [70]) and are more resistant to breakdowns in trust ([18]). Avatars (anthropomorphic virtual characters) can make online shopping experiences more enjoyable, and both avatars and anthropomorphic chatbots can increase purchase intentions ([27]; [31]; [72]). Anthropomorphic digital messengers can even be more persuasive than human spokespeople in some contexts ([63]) and can increase advertising effectiveness ([13]). Anthropomorphized digital devices can even become friends with their users ([56]), such that the consumer resists being disloyal by replacing the product ([12]), leading to greater customer brand loyalty.Although most evidence points to beneficial effects of anthropomorphism, there are drawbacks. For example, anthropomorphic helpers in video games reduce enjoyment of the gaming experience by undermining a players' sense of autonomy ([36]). Other research shows that for agency-oriented customers, brand anthropomorphism exaggerates the perceived unfairness of price increases ([38]) and hurts brand performance amid negative publicity ([54]). Low-power customers perceive risk-bearing entities (e.g., slot machines) as riskier when the entities are anthropomorphized ([37]). Further, research suggests that when customers are in crowded environments and want to socially withdraw, brand anthropomorphism harms customer responses ([53]). Thus, it would be overly simplistic to assume that anthropomorphism positively impacts customers' encounters with brands, products, or companies. The consequences are more nuanced, with outcomes depending on both customer characteristics and the context ([64]).While customers' downstream responses to anthropomorphism are mixed, one consistent consequence of anthropomorphism is that customers attribute more agency to anthropomorphic entities ([20]). ""Agency"" refers to the capacity to plan and act ([25]). Because anthropomorphism leads customers to perceive a mental state in another entity, it increases individuals' perception that the entity is capable of acting in a deliberate manner ([69]). This increases expectations that the agent has abilities such as emotion recognition, planning, and communication ([25]). These heightened expectations lead individuals to ascribe moral responsibilities to anthropomorphic entities ([69]), to believe that the entity should be held accountable for its actions ([18]), and to think the entity deserves punishment in the case of wrongdoing ([25]).Of course, anthropomorphic entities do not always perform in a manner consistent with the high levels of agency customers expect. In fact, some researchers suggest that one reason behind the ""uncanny valley"" (i.e., the tendency for a robot to elicit negative emotional reactions when it closely resembles a human; [48]) is because robots do not perform in the agentic manner that their human resemblance would imply ([69]). In other words, the robots' behavior violates the expectations elicited by their highly anthropomorphic facade. These violations arguably apply to current chatbots, given that their performance is not expected to reach believable levels of human intelligence before 2029 ([58]). Thus, expectancy violations play an important role in chatbot-driven customer service settings. Expectancy Violations and Customer AngerBefore using a product or service, customers form expectations regarding how they anticipate the target product, brand, or company will perform. Postusage, customers evaluate the target's performance and compare that to their preusage expectations ([10]). When performance fails to meet expectations, the negative disconfirmation is known as an expectancy violation ([62]), which arises because ( 1) preusage expectations are high or ( 2) postusage performance is poor ([10]). Expectancy violations not only harm customer satisfaction ([50]; [51]) but also negatively impact other consequential downstream outcomes, including attitude toward the company ([10]) and purchase intentions ([11]; [50]). Importantly, customer responses to expectancy violations are highly influenced by their emotional states, particularly anger ([ 4]).Two theories help explain why anger increases customers' negative responses to expectancy violations. The functionalist theory of emotion suggests that anger is an activating, high-intensity emotion with an evolutionary purpose: it evokes quick decision making and heuristic use to react quickly to immediate threat ([ 8]). Anger is often used as a strategy to respond to obstacles ([43]; [46]) or retaliate against an offending party ([14]) because of its tendency to increase action and aggression, compared with other emotions that are deactivating (e.g., sadness; [15]; [41]) or nonaggressive ([43]).This retaliation is also predicted by appraisal theorists, who suggest that even in situations of incidental anger, anger increases the tendency to hold others responsible for negative outcomes ([35]) and to respond punitively toward them ([23]; [42]; [43]). This is markedly distinct from emotions such as frustration or regret, which are more likely to manifest when people hold the situation or themselves responsible for negative outcomes, respectively ([21]; [55]). Thus, angry (vs. nonangry) customers are more likely to blame others and retaliate when another's performance falls short of expectations. This is particularly the case if their goals are obstructed ([46]), as angry customers especially feel the need to achieve a desirable outcome ([55]). Linking Chatbot Anthropomorphism, Expectancy Violation, and Customer AngerDrawing from the extant theories and research, we hypothesize that anthropomorphism heightens customers' preperformance expectations about a chatbot's level of agency and performance capabilities, resulting in expectancy violations. Further, angry customers are more likely to suffer from expectancy violations due to their need to overcome obstacles, to blame and hold others accountable, and to respond punitively to such expectancy violations due to their action orientation (i.e., giving lower satisfaction ratings, poor reviews, or withholding future business from the offending party). This logic would suggest that angry customers might be better served by nonanthropomorphic agents. Recent research supports this notion by demonstrating that in unpleasant service situations, reducing human contact (e.g., through technological barriers; [ 6]) can help attenuate customer dissatisfaction and limit negative service evaluations ([22]).Building on these arguments, we predict that individuals who enter a chatbot service interaction in an angry emotional state will respond negatively to chatbot anthropomorphism, whereas individuals in nonangry emotional states will not. While the most immediate negative reaction is likely to manifest in reduced customer satisfaction ratings of the service encounter with the chatbot, this can also carry over to harm more general firm evaluations and result in lower future purchase intentions, which are known consequences of dissatisfaction ([ 3]). Formally, we hypothesize the following: H1: For angry customers, chatbot anthropomorphism has a negative effect on (a) customer satisfaction, (b) company evaluation, and (c) purchase intention. This negative effect does not manifest for customers in nonangry emotional states. H2: Chatbot anthropomorphism leads to inflated expectations of chatbot efficacy, which, for angry customers, results in the negative effect described in H1.Our proposed conceptual framework is illustrated in Figure 1. Across five studies, using a combination of real-world and experimental data, we test the different parts of our theorizing to collectively support our proposed framework. In Study 1, we analyze a large data set from an international mobile telecommunications company that captures customers' interactions with a customer service chatbot. We use natural language processing (NLP) on chat transcripts and find that for customers exhibiting an angry emotional state during a chatbot-led service encounter, anthropomorphic treatment of the bot has a negative effect on their satisfaction with the service encounter (consistent with H1a). In Study 2, the first of four experiments, we manipulate chatbot anthropomorphism and customer anger and find that angry customers display lower customer satisfaction when the chatbot is anthropomorphic versus when it is not (consistent with Study 1 and H1a). Study 3 shows that the negative effect extends to company evaluations (H1b) but not when the chatbot effectively resolves the problem. Study 4 shows that the negative effect of chatbot anthropomorphism for angry customers extends further to reduce customers' purchase intentions (H1c) and provides evidence that this effect is driven by inflated preinteraction expectations of chatbot efficacy (H2). Finally, Study 5 manipulates preinteraction expectations and demonstrates that the negative effect dissipates when people have lower expectations of anthropomorphic chatbots (further supporting H2).Graph: Figure 1. Illustration of proposed model. Study 1Study 1 analyzes a real-world data set from an international mobile telecommunications company capturing customers' interactions with a customer service chatbot. The chatbot was available via the company's website and mobile app and was a text-only bot driven by machine learning, specifically, advanced NLP. The chatbot was highly anthropomorphic; the avatar was a cartoon illustration of a young female avatar with long hair, makeup, and modern casual clothing. Her name appeared in the chat, and customers could visit a profile webpage with her bio describing her personality and listing some of her likes and dislikes.The main purpose of the study was to examine how treating a chatbot as more or less human (i.e., higher or lower anthropomorphic treatment) impacted customer satisfaction with the encounter and, critically, whether this effect was moderated by customer anger (i.e., H1a). Because the chatbot was anthropomorphic and this could not be varied experimentally, we focused on the anthropomorphic treatment of the chatbot. If a customer treats a chatbot in a more human-consistent way, then we assume that is a consequence of a customer having more anthropomorphic thoughts resulting from perceiving the chatbot as more anthropomorphic. Specifically, we operationalized anthropomorphic treatment as the extent to which customers used the chatbot's name in their text-based conversation. As a name makes an object more anthropomorphic ([68]), the use of the chatbot's name indicates treating it as more human and serves as a reasonable proxy for anthropomorphic treatment. Data and MeasuresData were provided by a major international mobile telecommunications company. The data set covers 1,645,098 lines of customer text entries from 461,689 unique customer chatbot sessions that took place between September 2016 and August 2017 in one European country served by this company. At the end of each session, customers were given the option to rate their satisfaction with the chatbot encounter from one to five stars. Approximately 7.5% of sessions were rated (34,639 out of 461,689). In addition, for each line of customer text entered, there were metadata from the underlying chatbot NLP system that indicated the system's confidence in it having correctly ""understood"" each line of customer input, which was expressed as a percentage and termed the ""bot recognition rate."" We used the 1–5 satisfaction rating as the dependent variable. The distribution of this variable is shown in Figure 2, and the mean (SD) satisfaction rating was 2.16 (.79). We controlled for quality of the chatbot experience using the bot recognition rate, drawing on the assumption that for a given chatbot session, a higher average and lower variance in recognition rate indicated that the chatbot consistently understood more of a customer's inputs, which likely meant that the customer had an overall better communication experience.Graph: Figure 2. Distribution of user satisfaction ratings following interaction with anthropomorphic service chatbot.We processed chat transcript data (i.e., unstructured text) using the dictionary-based Linguistic Inquiry and Word Count (LIWC) package[ 4] ([52]) to classify each consumer text entry with respect to anger and to build our measure of the extent to which each customer treated the chatbot anthropomorphically. AngerIn line with our theorizing, anger was the key emotion in customers' inputs to the chatbot. Our measure of anger was the corresponding LIWC item (""anger"") that indicates the proportion of words in the input that are classified as being associated with anger. To arrive at the session-level measure, we averaged the LIWC anger value of each customer input within a given session. Unfortunately, this implicitly assumes that anger is exogenous by ignoring the initial emotional state of the customer and the dynamics of the consumer–bot exchange. We subsequently examine the robustness of our results when relaxing this assumption. Anthropomorphic treatmentThe chatbot was anthropomorphic because it had been endowed with extensive humanlike features (e.g., name, avatar, likes/dislikes). As we had no control over this, we instead aimed to measure anthropomorphic treatment, or the extent to which customers treated the chatbot in a humanlike manner.There are several possible measures to derive for anthropomorphic treatment. Our approach was to use a simple measure based on the frequency of use (or nonuse) of the chatbot's name, assuming that if a customer used the chatbot's name, then they were treating it in a more humanlike manner than if they did not use it. Thus, our measure of anthropomorphic treatment was the total number of times in a customer's chatbot session that they used the chatbot's name. Repetition of the chatbot's name may also be an implicit acknowledgement by a customer of the chatbot's agency, which is another key indicator of humanlike treatment. Examples of this are customer inputs such as, ""Hello [Bot Name], my name is [Customer Name], can you please help me with my bill?"" and finishing a conversation with ""Thank you [Bot Name]."" The mean (SD) of this count was.032 (.178), ranging from zero to six times the bot's name was used per user session. Bot recognition rateAs noted previously, the chatbot's NLP system produced a confidence value, expressed as a percentage, of the likelihood that it correctly understood customer text input. The average and standard deviation of these values within a user session provide control variables for the quality and consistency of that customer experience. The mean (SD) of this value was 73.09 (23.6). Number of interactionsAs a control, we captured the number of times the customer interacted with the chatbot in each session (i.e., the number of customer inputs per session). The mean (SD) was 3.56 (4.21), with a range of 1 to 491. Chatbot interaction typeFinally, we controlled for the type of interaction the customer had with the service chatbot. This categorization was used by the chatbot itself, retained as metadata, connecting the requests received with a broad categorization of different types of service encounters: General Dialog, Questions and Answers, Providing Links, Frequently Asked Questions, and Feedback. Examples of individual dialogs include ""Invoices,"" ""SIM Card Activation,"" and ""PIN Recovery."" AnalysisOur goal was to estimate the extent to which anthropomorphic treatment, anger, and their interaction affected customer satisfaction. Considering that satisfaction was measured on a 1–5 scale (with five being the highest level of satisfaction), but with a great deal of mass in the distribution at the scale midpoint and both endpoints, we treated this as an ordinal variable and analyzed it using an ordinal logistic regression. Thus, we accounted for the potential for heterogeneity in distances between scale points. Vitally, we econometrically handled the obvious potential for a selection bias because only 7.5% of all customer chat sessions in our data set included a satisfaction rating. Thus, our analysis is based on the 34,639 chat sessions for which we had a satisfaction measure, but we make use of all 461,689 chat sessions in our treatment of endogenous sample selection.To account for the ordinal nature of our data and the sample selection, we used an extended ordinal probit model, which estimates the probit selection and the ordinal satisfaction ratings equations simultaneously and with correlated errors ([26]).[ 5] The first equation was a binary probit model for leaving a satisfaction rating ( 1) or not (0), as described in Equation 1 (with i denoting the chat session and error esi). The second equation was an ordered probit model, as shown in Equation 2 (with i denoting the chat session and error ei). The error terms in Equations 1 and 2 were correlated. Note that in Equation 1 we used bot interaction type as an exclusion restriction because it impacts the likelihood of leaving a rating, P(Feedback = 1)i, but not the satisfaction rating, Ratingi. P(Feedback=1)i=α0+α1(AnthropomorphicTreatment)i+α2(Anger)i+α3(BotLanguageRecognitionRate)i+α4(BotLanguageRecognitionVariance)i+α5(NumberofInteractionswithBot)i+α6(BotInteractionType)i+esi, Graph( 1) Ratingi=β0+β1(AnthropomorphicTreatment)i+β2(Anger)i+β3(AnthropomorphicTreatment×Anger)i+β4(BotLanguageRecognitionRate)i+β5(BotLanguageRecognitionVariance)i+β6(NumberofInteractionswithBot)i+ei. Graph( 2) ResultsTable 1 reports descriptive statistics and correlations. Table 2 reports results from the model described previously. First, considering the selection model in Table 2, we see that the type of customer–bot interaction (which we use as an exclusion restriction) is a significant predictor of the likelihood of the customer providing feedback to the firm, especially when, compared with general conversation (the base case in the model), the bot is focused on eliciting feedback (α6-feedback = 2.973, p <.001). Anger had a negative and significant effect on the likelihood of providing feedback (α2 = −.008, p <.05), whereas the number of exchanges with the bot during the session had a positive and significant effect (α5 = .033, p <.001).GraphTable 1. Descriptive Statistics in Study 1. 1 Notes: Boldface indicates p < .05.GraphTable 2. Probit Selection Model—Likelihood of Customer Providing a Rating After Interaction and Ordinal Probit—Customer Rating (Study 1). 2 †p < .10.3 *p < .05.4 **p < .01.5 ***p < .001.Next, considering the main model for satisfaction ratings in Table 2, after accounting for the likelihood of providing feedback, both main effects of anthropomorphic treatment and anger were nonsignificant (β1 = −.055, n.s.; β2 = −.002, n.s.). However, their interaction was significant and negative (β3 = −.167, p = .05). This was after controlling for the technical performance of the bot during that session (recognition rate mean and variance) and the number of customer interactions in a session. Probing this interaction revealed the hypothesized effects across the distribution of consumer anger scores. When anger is higher (1 SD above the mean), the marginal effect of anthropomorphic treatment on satisfaction rating is significant and negative (β1 = −.350, p = .02), consistent with H1a. Interestingly, we also found that when anger was lower, but still present (1 SD below the mean), the marginal effect of anthropomorphic treatment on satisfaction rating remained negative, albeit with a smaller effect size than in the higher anger case (β1 = −.329, p = .02). Thus, it appears that the mere presence of anger can result in a negative relationship between anthropomorphic treatment and satisfaction. Further probing this interaction, we found that when anger was zero (i.e., not at all present), the marginal effect of anthropomorphic treatment on satisfaction rating was nonsignificant (β1 = .011, p = .32). Robustness ChecksWe were restricted in our analysis of Study 1 given that the data were provided as an outcome of the firm's operations and the conditions around each customer could not be assigned or manipulated. We could not control whether a customer provided feedback, the level of anthropomorphic treatment, or the customers' level of anger upon entering the chatbot interaction. While the first two limitations were addressed with a selection model and by exploiting variance in exhibited behavior, the levels of anger were taken as a given.As a robustness check, we instead considered anger as a binary treatment effect and estimated an additional augmented model that accounted for the ordinal nature of ratings, the selection bias in providing feedback, and the anger of customers as a treatment condition. The inherent weakness of this model comes from a loss of information by dichotomizing anger into a binary (angry/not angry) condition, thus losing the nuance of levels of anger interacting with the level of anthropomorphic treatment. However, as a check, it shows the robustness of results to the specification of anger as an endogenous component of the customer experience, and for the estimation of drivers of anger, again with correlated errors (for the outcome, selection, and anger).The model and results are presented in Web Appendix B. The endogenous anger treatment was positively but weakly correlated with the decision to provide feedback (r = .047) and negatively correlated with satisfaction rating (r = −.449). In the endogenous anger condition model, anthropomorphic treatment was positive and significant (γ1 = .289, p < .001). Critically, in the model for satisfaction rating, anthropomorphic treatment was nonsignificant for customers in the nonangry treatment (β1a = −.059, n.s.) and negative and significant for customers in the angry treatment (β1b = −.573, p = .04). We confirmed the significant difference between those two groups using a Wald test (χ2( 2) = 6.62, p = .04), which highlights that even if we model anger as a binary outcome of an endogenous process, our conclusions remain essentially the same.In addition, we test whether alternative negative emotions (anxiety and sadness) or a positive mood valence meaningfully explain our customer satisfaction ratings or interact with the degree of chatbot anthropomorphic treatment. A reestimated extended ordinal probit model containing additional emotions is presented in Web Appendix C. While sadness is a significant predictor in our first stage model of feedback selection, no other negative emotions were significant. However, positive valence interacts meaningfully with anthropomorphic treatment (β = .0508, p = .001) in explaining satisfaction, consistent with prior research showing positive consequence of anthropomorphism. But, importantly, the hypothesized effect between anger and anthropomorphic treatment remains unchanged (β = −.1617, p = .05). DiscussionBy leveraging real-world data from customers actively engaging with a chatbot across numerous chat sessions, we find initial evidence in support of H1a. An increase in the average level of anger exhibited by the consumer during their session resulted in a lower level of satisfaction with the service encounter, but only when the chatbot was treated anthropomorphically. In situations where the bot was not treated anthropomorphically, higher levels of anger did not meaningfully affect consumer satisfaction. Of course, this study has limitations. First, all customers were presented with the same highly anthropomorphic chatbot, so we had to rely on the variance in customers' anthropomorphic treatment of the bot, as opposed to variation in chatbot anthropomorphism, per se. Second, we initially assumed that customers entered the chat angry, independent from their exchange with the chatbot; however, our robustness checks confirm that anger is not strictly exogenous but also arose out of characteristics of the exchange with the bot (e.g., the number of exchanges, variance in language recognition). Finally, both anthropomorphic treatment and anger were measured from customer behaviors, rather than manipulated. These limitations motivated the four follow-up experiments. Study 2The purpose of Study 2 was to test our theory under a controlled experimental setting and further show that, for angry customers, chatbot anthropomorphism has a negative effect on customer satisfaction. Accordingly, this study manipulated both chatbot anthropomorphism (via the presence/absence of anthropomorphic traits in the chatbot) and customer anger, allowing us to infer a causal relationship on satisfaction. In addition, careful chatbot selection enabled us to rule out idiosyncratic features of the Study 1 chatbot. Specifically, two of its specific features pose potential confounds in trying to generalize the results. First, it was clearly female, and previous research suggests that female service employees are more often targets of expressed frustration and anger from customers than are male service employees ([59]). In addition, she had a smiling expression, which is incongruent with the emotional state of participants who were angry, and such affective incongruity may cause a negative reaction and lower satisfaction. Pretests AvatarWe pretested avatars to select one that was both gender and affectively neutral. Twenty-five participants from Amazon Mechanical Turk (MTurk) evaluated a series of avatars on bipolar scales assessing both gender (""definitely male–definitely female"") and warmth (""extremely cold–extremely warm"") and indicated their agreement with one seven-point Likert item: ""This avatar has a neutral expression."" Drawing on the results of this pretest, we used the avatar pictured in Web Appendix D for the anthropomorphic chatbot condition. Specifically, our analysis confirmed that this avatar was neutral in both gender and warmth, with scores that did not significantly differ from the corresponding scale midpoints (Mgender = 3.64, t(24) = −1.23, p = .23; Mwarmth = 3.80, t(24) = −1.16, p = .26) and agreement with the neutral expression item was significantly above the midpoint (M = 5.76, t(24) = 7.80, p < .001).We also wanted to choose a gender-neutral name for the anthropomorphic chatbot. Twenty-seven participants from MTurk evaluated a series of names on a seven-point bipolar scale assessing gender (""definitely male–definitely female""). From the results, we chose the name ""Jamie,"" which was not significantly different from the midpoint (M = 3.89, t(26) = −.68, p = .50). ScenarioWe created two customer service scenarios (neutral vs. anger) to use in this study (for the full scenarios used in both conditions, see Web Appendix E). In the neutral condition, the scenario described how the participant had purchased a camera for an upcoming trip, but upon receipt, the camera was broken. After searching the website, they diagnosed the issue as a problem with the lens and read about how to exchange the camera. It must be mailed back to the company before receiving a new camera, which is expected to arrive after they depart for a trip, the reason they wanted it originally.In the anger condition, the scenario contained additional details designed to evoke anger. The original camera shipping was delayed, diagnosing the issue was time consuming, and they already tried to contact customer service and were placed on hold and passed from one representative to another. To ensure this scenario was successful in invoking anger compared with the neutral condition but did not differ in realism, we conducted a pretest on MTurk. Fifty participants were randomly assigned to read one of the two scenarios and then indicated how angry the scenario would make them feel (two items: ""This situation would leave me feeling angry [frustrated]""; r = .65) and how realistic they found the scenario (two items: ""How realistic [true-to-life] is this scenario?""; r = .61) on seven-point Likert scales (1 = ""not at all,"" 7 = ""extremely""). Our analysis confirmed that those in the angry condition reported significantly greater feelings of anger than those in the neutral condition (Mneutral = 5.46 vs. Manger = 6.06; F( 1, 48) = 4.31, p = .04). However, there was no significant difference in scenario realism (Mneutral = 5.48 vs. Manger = 5.69; F( 1, 48) = 2.09, p = .16). AnthropomorphismWe created two versions of the customer service chatbot (control vs. anthropomorphic). In the control condition, participants were told they would interact with ""the Automated Customer Service Center,"" and in the anthropomorphic condition, they were told they would interact with ""Jamie, the Customer Service Assistant."" Furthermore, in the anthropomorphic condition, the chatbot featured the avatar selected from the pretest, and the chat text consistently used a singular first-person pronoun (i.e., ""I"") and appeared in quotation marks.To ensure that this manipulation was successful, we conducted a pretest on MTurk. One hundred one participants were randomly assigned to one of the two chatbots and then indicated how anthropomorphic the chatbot was on nine seven-point Likert scales (adapted from [19]] and [38]]: ""Please rate the extent to which [the Automated Customer Service Center/Jamie]: came alive (like a person) in your mind; has some humanlike qualities; seems like a person; felt human; seemed to have a personality; seemed to have a mind of his/her own; seemed to have his/her own intentions; seemed to have free will; and seemed to have consciousness""; α = .98). Analysis confirmed that those in the anthropomorphic condition reported significantly greater anthropomorphic thought (Mcontrol = 3.37 vs. Manthro = 4.82; F( 1, 99) = 16.64, p < .001). Main Study Design and ProcedureTwo hundred one participants (48% female; Mage = 37.29 years) from MTurk participated in this study in exchange for monetary compensation. The study consisted of a 2 (chatbot: control vs. anthropomorphic) × 2 (scenario emotion: neutral vs. anger) between-subjects design. Participants were randomly assigned to read one of the aforementioned scenarios (neutral or anger). Then, participants entered a simulated chat with either ""the Automated Customer Service Center"" in the control chatbot condition or ""Jamie, the Customer Service Assistant"" in the anthropomorphic condition.In the simulated chat, participants were first asked to open-endedly explain why they were contacting the company. In addition to serving as an initial chatbot interaction, this question also functioned as an attention check, allowing us to filter out any participants who entered nonsensical (e.g., ""GOOD"") or non-English answers ([17]). Subsequently, participants encountered a series of inquiries and corresponding drop-down menus regarding the specific product they were inquiring about (camera) and issue they were having (broken and/or damaged lens). They were then given return instructions and indicated they needed more help. Using free response, they described their second issue and answered follow-up questions from the chatbot about the specific delivery issue (delivery time is too long) and reason for needing faster delivery (product will not come in time for a special event). Finally, participants were told that a service representative would contact them to discuss the issue further. The interaction outcome was designed to be ambiguous (representing neither a successful nor failed service outcome; however, we manipulate this outcome in Study 3). The full chatbot scripts for both conditions and images of the chat interface are presented in Web Appendices F and G.Upon completing the chatbot interaction, participants indicated their satisfaction with the chatbot by providing a star rating (a common method of assessing customer satisfaction; e.g., [61]) between one and five stars, on five dimensions (α = .95): overall satisfaction, customer service, problem resolution, speed of service, and helpfulness. Lastly, participants indicated their age and gender and were thanked for their participation. Results and DiscussionFour participants failed the attention check (entering a nonsensical response for the open-ended question), leaving 197 observations for analysis. Analysis of variance (ANOVA) results revealed a significant main effect of scenario emotion on satisfaction (i.e., averaged star rating on the five dimensions), in that those in the anger scenario condition were less satisfied than those in the neutral scenario condition (F( 1, 193) = 33.45, p < .001). Importantly, we found a significant chatbot × scenario emotion interaction on customer satisfaction (F( 1, 193) = 5.26, p = .02). Consistent with Study 1, a simple effects test revealed that participants in the anger scenario condition were less satisfied when the chatbot was anthropomorphic (M = 2.09) versus when it was not (M = 2.58; F( 1, 193) = 4.13, p = .04). For those in the neutral scenario, chatbot anthropomorphism had no significant influence on satisfaction, but satisfaction was directionally higher in the anthropomorphic condition (Mcontrol = 3.16 vs. Manthro = 3.44; F( 1, 193) = 1.46, p = .23). Figure 3 presents an illustration of means.Graph: Figure 3. The effect of chatbot anthropomorphism and anger on customer satisfaction (Study 2).Whereas Study 1 provides initial support for the interactive effect of anthropomorphism and anger on customer satisfaction, Study 2 tests our theorizing in a controlled experimental design. This allowed us to more definitively conclude that when customers are angry, anthropomorphic traits in a chatbot lower customer satisfaction with the chatbot (consistent with H1a)[ 6] and rule out alternative explanations based on the chatbot's gender or expression. While not central to our main theorizing, we ran an identical study manipulating sadness instead of anger. Both anger and sadness are negative emotions, but anger represents an activating emotion, whereas sadness is a deactivating emotion ([15]; [41]). We predicted that only angry customers are activated to respond negatively to anthropomorphic chatbots due to their need to overcome obstacles, blame others, and respond punitively to expectancy violations ([23]; [43]; [42]). Interestingly, participants in the sad condition were more satisfied when the chatbot was anthropomorphic versus when it was not (Mcontrol = 1.90 vs. Manthro = 2.53; F( 1, 188) = 8.49, p < .01), which is consistent with prior literature ([27]; [72]) that demonstrates the positive effect of anthropomorphic chatbots in other situations. Full details are available in Web Appendix I. Study 3There were three main goals of Study 3. First, while our previous study induced anthropomorphism via a simultaneous combination of visual and verbal cues (with an avatar and first-person language, respectively), the current study aimed to show that the effect diminishes with the reduction of anthropomorphic traits. Thus, we remove the visual trait of anthropomorphism (i.e., the avatar) and anticipate the negative effect of anger to attenuate, providing further support that the degree of humanlikeness is responsible for driving the effect. Second, we wanted to test H1b by exploring whether the negative effect of anthropomorphism for angry customers would extend to influence their evaluations of the company itself. Finally, we wanted to provide initial evidence that expectancy violations are responsible for these observed negative effects. To do so, we examined whether the outcome of the chat interaction—namely, if the chatbot was able to indubitably resolve the customer's concerns—could serve as a boundary condition. We predicted that if the chatbot could meet the high expectations of efficacy, the negative effect of anthropomorphism should dissipate. Pretests AvatarWe selected a new avatar in this study to increase the robustness of our examination and generalizability of our findings. Twenty-six participants from MTurk evaluated a series of avatars as in the Study 2 pretest. Our analysis confirmed that the avatar (pictured in Web Appendix D) was considered neutral in both gender and warmth (Mgender = 4.04, t(25) = .12, p = .90; Mwarmth = 4.27, t(25) = 1.32, p = .20) and had a neutral expression (M = 5.12, t(25) = 4.35, p < .001). ScenarioAs in Study 2, we created two customer service scenarios (neutral vs. anger; for the full scenarios, see Web Appendix J). In the neutral condition, the scenario described a situation where the participant was interested in buying a camera from the company, ""Optus Tech,"" with a specific feature (advanced video stabilization). After searching the website, it was difficult to tell whether Optus Tech's camera had this feature. In addition, the delivery window was wide, which meant that the expected delivery may or may not occur after they depart for a trip, the whole reason they wanted the camera.In the anger emotion condition, there were additional details designed to evoke anger: researching the feature was time consuming, they already tried to contact customer service and were placed on hold and passed from one representative to another, the representative could not answer their question, and they had to call a second time to ask about shipping. To ensure that this scenario was successful in invoking anger compared with the neutral condition, we pretested 48 MTurk participants who were randomly assigned to read one of the two scenarios and then indicated both their anticipated feelings of anger and how realistic they found the scenario (as measured in the Study 2 pretest; r = .77 and r = .63, respectively). Indeed, those in the anger condition reported significantly greater anticipated feelings of anger than those in the neutral condition (Mneutral = 3.63 vs. Manger = 5.46, F( 1, 46) = 18.00, p < .001). However, there was no significant difference in how realistic participants found the two scenarios (Mneutral = 5.50 vs. Manger = 4.92, F( 1, 46) = 2.54, p = .12). We only used the anger scenario in this study, but an upcoming study used both scenarios. AnthropomorphismWe created three versions of the customer service chatbot: control, verbal anthropomorphic, and verbal + visual anthropomorphic. The first and last chatbots were similar to Study 2 except using the new avatar. The additional chatbot used the verbal anthropomorphic traits (i.e., the bot introduced itself as Jamie and used first-person language in quotations) but not the visual trait (i.e., the avatar). One hundred twenty-one MTurk participants were randomly assigned to one of the three chatbots: the Automated Customer Service Center (control chatbot condition), Jamie without an avatar (verbal anthropomorphic condition), or Jamie with an avatar (verbal + visual anthropomorphic condition) and then indicated how anthropomorphic the chatbot was on nine seven-point Likert scales (as in Study 2; α = .97). We coded the control, verbal anthropomorphic, and verbal + visual anthropomorphic conditions as 0, 1, and 2, respectively, to represent the strength of the anthropomorphic manipulation. Results demonstrated that the linear trend was significant (Mcontrol = 2.38 vs. Mverbal = 4.14 vs. Mverbal + visual = 4.39; F( 1, 118) = 39.16, p < .001). Main Design and ProcedureFour hundred nineteen participants (61% female; Mage = 38.50 years) from MTurk participated in this study in exchange for monetary compensation. This study consisted of a 3 (chatbot: control vs. verbal anthropomorphic vs. verbal + visual anthropomorphic) × 2 (scenario outcome: ambiguous vs. resolved) between-subjects design. First, all participants read the anger scenario and then entered a simulated chat with the chatbot. In the ambiguous outcome condition, participants encountered a series of questions and corresponding drop-down menus regarding the specific product (camera) and feature (advanced video stabilization) they were inquiring about. They were then given basic product information about the feature that was purposefully ambiguous. Then, participants indicated they needed more help. Using free response, they described their second issue and answered follow-up questions from the chatbot about the specific delivery issue (delivery window/timing) and reason for needing faster delivery (product will not come in time for a special event). Participants were told that a service representative would contact them to discuss the issue further. In the resolved outcome condition, there were two critical differences: participants were directly given the product feature information that resolved their query and explicitly informed of the specific delivery time information, which confirmed they would receive their delivery in time for their special event. The entire chatbot scripts for both conditions and images of the interface are presented in Web Appendices K and L.Upon completing the interaction, participants evaluated the company, Optus Tech, on four seven-point bipolar items (α = .95): ""unfavorable–favorable,"" ""negative–positive,"" ""bad–good,"" and ""unprofessional–professional."" As a manipulation check for the scenario outcome, participants responded to three items: ""My question was sufficiently answered,"" ""My problem was appropriately resolved,"" and ""I got the help I needed"" (1 = ""strongly disagree,"" and 7 = ""strongly agree""; α = .97). To assess whether participants knew they were interacting with a chatbot (vs. a human), we asked participants to indicate the extent to which they felt they interacted with a human versus an automated chatbot (1 = ""definitely a real live human,"" 7 = ""definitely an automated chatbot"".[ 7] Participants indicated demographics and were thanked for participating. Results and DiscussionFifty-two participants failed the attention check (entering a nonsensical response for the open-ended question), leaving 365 observations for analysis. Scenario outcome manipulation checkParticipants in the resolved condition indicated that their problem was more appropriately resolved (M = 6.36) than participants in the ambiguous condition (M = 4.32; t(363) = 12.40, p < .001), indicating a successful manipulation. Main analysisUnsurprisingly, ANOVA results revealed a significant main effect of scenario outcome on company evaluation, such that participants reported lower evaluations of the company when the outcome was ambiguous (M = 4.68) versus when it was resolved (M = 5.36; F( 1, 359) = 18.44, p < .001). There was no main effect of chatbot anthropomorphism (F( 1, 359) = 1.38, p = .25). Importantly, there was a marginally significant chatbot anthropomorphism × anger scenario interaction on company evaluation (F( 2, 359) = 2.64, p = .07). A simple effects test revealed that there was no significant difference between the chatbot conditions when the outcome was resolved (F < 1). This provides some evidence that effectively meeting expectations eliminates the negative effect, which is conceptually consistent with H2, because if high preinteraction expectations of efficacy are met, there should be no resultant expectancy violations. However, there was a significant difference when the outcome was ambiguous (F( 2, 359) = 3.78, p = .02). Planned contrasts revealed when the outcome was ambiguous, participants reported lower company evaluations when the chatbot was verbally and visually anthropomorphic (M = 4.28) versus the control (M = 5.06; t(359) = 2.75, p < .01), providing evidence in support of H1b. However, the verbal anthropomorphic condition (without an avatar) did not significantly differ from the control (Mverbal = 4.69 vs. Mcontrol = 5.06; t(359) = 1.36, p = .17) or the verbally and visually anthropomorphic condition (vs. Mverbal + visual = 4.28; t(359) = 1.48, p = .14). Because the verbal anthropomorphic condition both theoretically and empirically fell between the two other conditions, we tested whether our anthropomorphism manipulation demonstrated a linear trend. We coded the control, verbal anthropomorphic, and verbal + visual anthropomorphic conditions as 0, 1, and 2, respectively, to represent the strength of the anthropomorphic manipulation. Results demonstrated that the linear trend was not significant when the outcome was resolved (F < 1) but was significant when the outcome was ambiguous (F( 1, 359) = 7.55, p < .01). These findings suggest that visual and verbal anthropomorphic traits likely produce an additive effect, where multiple traits lead to greater anthropomorphic thought, and accordingly results in lower company evaluations (at least in the case of angry consumers, which we exclusively examined in this study). Figure 4 presents an illustration of means.Graph: Figure 4. The effect of chatbot anthropomorphism and anger on company evaluation (Study 3). Study 4Study 4 serves two key purposes. First, we extend our investigation to an even further downstream negative outcome by examining purchase intentions (H1c). Second, we build on the findings of Study 3 and directly test our proposed underlying process: expectancy violations driven by preperformance expectations (H2). Specifically, we predict anthropomorphism increases preperformance expectations that a chatbot would display greater agency and performance. While people in a neutral state will perceive the expectancy violation, they are less motivated to retaliate or respond punitively. Angry people, in contrast, punish the company by lowering their purchase intentions. Design and ProcedureOne hundred ninety-two participants (55% female; Mage = 37.31 years) from MTurk participated in exchange for payment. This study consisted of a 2 (chatbot: control vs. anthropomorphic) × 2 (scenario emotion: neutral vs. anger) between-subjects design. Participants were randomly assigned to read one of the neutral or anger information search scenarios pretested in the prior study. Then participants were told they were about to enter a chat with either the Automated Customer Service Center (control condition) or Jamie (anthropomorphic condition). At this point, all participants saw the brand logo for Optus Tech, but those in the anthropomorphic chatbot condition also saw the avatar.Next, participants indicated their preinteraction efficacy expectations regarding the chatbot's upcoming performance on four seven-point Likert items (""I expect the Automated Service Center/Jamie to: do something for me; take action; be proactive in resolving my issues; say things to calm me down""; α = .89). Participants completed the same interaction as in the ambiguous condition from Study 3 and indicated their purchase intentions for the camera on two seven-point Likert items: ""I would buy the camera from Optus Tech,"" and ""I would try to find a different company to buy the camera from"" (the latter was reverse-coded; r = .65). Afterward, participants rated their postinteraction assessment of the chatbot's efficacy, on four seven-point Likert items that corresponded to the preinteraction items (""I felt the Automated Service Center/Jamie: did a lot for me; took action; was proactive in resolving my issues; said things to calm me down""; α = .92). Lastly, participants indicated their age and gender and were thanked for their participation. Results and Discussion Purchase intentionTwenty-one participants failed the attention check used in prior studies, leaving 171 observations for analysis. ANOVA results revealed a significant main effect of anger on purchase intentions, where participants in the anger scenario condition reported lower purchase intentions than those in the neutral scenario condition (F( 1, 167) = 20.04, p < .001). Consistent with the pattern of results predicted in H1c, there was a significant chatbot anthropomorphism × anger scenario interaction on purchase intentions (F( 1, 167) = 4.29, p = .04). A simple effects test revealed that participants in the anger scenario condition reported lower purchase intentions when the chatbot was anthropomorphic (M = 2.73) versus when it was not (M = 3.57; F( 1, 167) = 5.79, p = .02). For those in the neutral scenario, the chatbot had no significant influence on purchase intentions. Figure 5 presents an illustration of means.Graph: Figure 5. The effect of chatbot anthropomorphism and anger on purchase intentions (Study 4). Expectancy violationWe predicted that encountering an anthropomorphic chatbot at the start of the service experience would increase participants' preinteraction expectations about the efficacy of the chatbot, relative to the control chatbot. However, the postinteraction efficacy assessments of the chatbots should not differ because they performed equally, resulting in greater expectancy violations for anthropomorphic chatbots (H2).To assess this hypothesis, we ran a repeated-measures ANOVA with chatbot anthropomorphism as the between-subjects variable and time (preinteraction expectations at Time 1 and postinteraction assessments at Time 2) as the within-subjects factor. We did not find a significant overall main effect of chatbot anthropomorphism on efficacy (F( 1, 169) = .91, p = .34). Importantly, there was a significant interaction of chatbot anthropomorphism and time (F( 1, 169) = 7.31, p = .01). Probing this interaction, as we expected, preinteraction expectations of the chatbot's efficacy at Time 1 were significantly higher in the anthropomorphism condition than in the control condition (Mcontrol = 4.94 vs. Manthro = 5.50; F( 1, 169) = 6.91, p = .01), but there was no difference in the postinteraction assessments at Time 2 (Mcontrol = 4.09 vs. Manthro = 3.88; F < 1). These results are consistent with the logic that a greater expectancy violation is more likely in the anthropomorphism condition than in the control because of inflated preinteraction expectations of chatbot efficacy stemming from more humanized traits.We also calculated an expectancy violation score for each participant by subtracting their postinteraction assessment score at Time 2 from their preinteraction expectation score at Time 1 ([45]). As we expected, an ANOVA with chatbot anthropomorphism and anger scenario as predictors and expectancy violation as the dependent variable produced only a significant main effect of chatbot anthropomorphism on expectancy violation (Mcontrol = .85 vs. Manthro = 1.62; F( 1, 169) = 7.31, p = .01). MediationImportantly, our theorizing suggests that anthropomorphism inflates preinteraction expectations of chatbot efficacy for all customers. Yet, angry customers are more motivated than nonangry customers to respond punitively by lowering their purchase intent. Accordingly, we performed a moderated mediation analysis based on 10,000 bootstrapped samples ([29], Model 15). While the index of moderated mediation did not reach significance (indirect effect = .0279; 95% confidence interval [CI]: [−.0778,.1562]), we examined the separate indirect effects at each emotion condition based on our a priori predictions ([ 2]; [30]). In other words, while we did not have predictions for what might drive purchase intention for those in the neutral condition, we did predict that for angry customers, lowered preinteraction expectations would explain the decreased purchase intention. As per our theorizing, results demonstrated that for individuals in the anger condition, preinteraction expectations mediated the effect of chatbot anthropomorphism on purchase intention (indirect effect = .0675; 95% CI: [.0012,.1707]). However, for participants in the neutral condition, the indirect effect was not significant (indirect effect = .0396; 95% CI: [−.0408,.1468]). These results suggest that, as we predicted, the inflated preinteraction expectation of efficacy caused by the anthropomorphic chatbot is the underlying mechanism lowering purchase intentions for angry participants. Study 5Study 4 demonstrated that anthropomorphic chatbots result in lower purchase intentions when customers are angry by elevating preinteraction expectations of efficacy. Yet, it is theoretically and managerially important to understand how this effect can be remedied. Some companies attempt to explicitly temper customer expectations of their chatbots. For example, Slack's chatbot introduces itself by explaining that, ""I try to be helpful (But I'm still just a bot. Sorry!)"" ([65]). Study 5 explores whether explicitly lowering customer expectations of anthropomorphic chatbots prior to the interaction effectively reduces negative customer responses. Avatar PretestFor the Study 5 pretest, 31 participants from MTurk evaluated a series of avatars as in the prior pretests. Our analysis confirmed that the avatar (see Web Appendix D) was considered neutral in both gender and warmth (Mgender = 5.55, t(30) = 1.52, p = .14; Mwarmth = 4.97, t(30) = −.19, p = .85) and had a neutral expression (M = 5.94, t(30) = 8.91, p < .001). Design and ProcedureThree hundred two participants (52% female; Mage = 40.78 years) from MTurk participated in exchange for monetary compensation. The study consisted of a 2 (chatbot: control vs. anthropomorphic) × 2 (expectation: baseline vs. lowered) between-subjects design. All participants read the anger information search scenario. Afterward, participants saw they would chat with either ""the Automated Customer Service Center"" in the control or ""Jamie, the Customer Service Assistant"" in the anthropomorphic chatbot condition. In the lowered expectation condition, they also read, ""The Automated Customer Service Center/Jamie, the Customer Service Assistant will do the best that it/I can to take action but sometimes the situation is too complex for it/me (it's/I'm just a bot) so please don't get your hopes too high.""Participants then indicated their preinteraction efficacy expectations (as in Study 4; α = .89), completed the product information interaction and evaluated the company (as in Study 3; α = .97), rated their postinteraction assessment of the chatbot's efficacy (as in Study 4; α = .92), answered demographic questions, and were thanked for their participation. Results and Discussion Expectancy violation manipulation checkConsistent with the manipulation intention, there was a main effect of anthropomorphism on preinteraction expectations (F( 1, 298) = 4.36, p = .04), where participants had higher expectations when the chatbot was anthropomorphic (M = 4.53) compared with the control (M = 4.18). There was also a main effect of expectations on preinteraction expectations (F( 1, 298) = 45.61, p < .001), where, as we intended, lowering expectations resulted in lower preinteraction expectations (M = 3.78) than in the baseline expectation condition (M = 4.93). There was a significant chatbot × expectation interaction on preinteraction expectations (F( 1, 298) = 7.00, p < .01). Simple effects tests revealed that in the baseline expectation conditions, the people in the anthropomorphic condition had higher expectations of chatbot efficacy than in the control condition (Mcontrol = 4.53 vs. Manthro = 5.34; F( 1, 298) = 11.35, p = .001). Yet, in the low-expectation conditions, there was no difference between the preinteraction expectations of efficacy for the anthropomorphic and control chatbot (Mcontrol = 3.83 vs. Manthro = 3.73; F < 1). For postinteraction evaluations, consistent with predictions, there were no significant differences (i.e., no main effect of anthropomorphism, no main effect of expectation, and no interaction between anthropomorphism and expectation). This indicates preinteraction expectations are responsible for changes to expectancy violations.Expectancy violations were calculated by subtracting postinteraction evaluations from preinteraction expectations, with higher numbers indicating greater violations. There is a main effect of anthropomorphism on expectancy violations (F( 1, 298) = 7.38, p < .01), where participants indicated greater expectancy violations when the chatbot was anthropomorphic (M = .65) compared with the control (M = .10). There was also a main effect of the expectation manipulation on expectancy violations (F( 1, 298) = 36.73, p < .001), where there were greater expectancy violations in the baseline expectation condition (M = .99) compared with the lowered-expectation condition (M = −.24). Importantly, there was also a significant chatbot × expectation interaction on expectancy violations (F( 1, 298) = 13.26, p < .001). As we expected, when participants had the baseline expectation (i.e., no information given), they experienced greater expectancy violations driven by preinteraction expectations when the chatbot was anthropomorphic compared with the control (Mcontrol = .35 vs. Manthro = 1.63; F( 1, 298) = 20.47, p < .001). Yet when participants were told to have lower expectations, there was no difference between the expectancy violations for the anthropomorphic chatbot and the control (Mcontrol = −.14 vs. Manthro = −.33; F < 1). Company evaluationThe ANOVA results revealed a marginal main effect of anthropomorphism on company evaluation; participants reported marginally lower evaluations of the anthropomorphic chatbot (M = 4.11) versus control (M = 4.44; F( 1, 298) = 2.86, p = .09). There was no main effect of expectation (F < 1). There was a significant chatbot × expectation interaction on company evaluation (F( 1, 298) = 4.35, p = .04). Consistent with prior studies, a simple effects test revealed that participants in the baseline expectation condition rated the company lower when the chatbot was anthropomorphic (M = 3.90) versus when it was not (M = 4.63; F( 1, 298) = 4.13, p = .04). For those in the lowered-expectation condition, chatbot anthropomorphism had no significant influence on company evaluations (Mcontrol = 4.25 vs. Manthro = 4.32; F < 1), indicating that lowering customer expectations of anthropomorphic chatbots effectively mitigated the negative effect of anger on company evaluations. Figure 6 presents an illustration of means, and Web Appendix M provides additional analysis.Graph: Figure 6. The effect of chatbot anthropomorphism and expectations on company evaluation (Study 5). General DiscussionThe deployment of chatbots as digital customer service agents continues to accelerate as the underlying machine learning technologies improve and as the practice becomes more common across industries. Customers are increasingly interacting with firms through chatbots, and there has been a significant push for more humanlike versions of such bots. Prior research has begun to demonstrate some negative implications of anthropomorphism in specific situations, including video games ([36]), gambling ([37]), and overcrowded environments ([53]), as well as for some types of people (agency-oriented customers; [38]). Yet, our research is the first to demonstrate the negative effect of anthropomorphism in the wider context of customer service and connect the use of these humanlike chatbots to negative firm outcomes.We find, using a large data set of real-world customer interactions and four experiments, that anthropomorphic chatbots can harm firms. An angry customer encountering an anthropomorphic (s. nonanthropomorphic) chatbot is more likely to report lower customer satisfaction, lower overall evaluation of the firm, and lower future purchase intentions. This negative effect is driven by expectancy violations due to inflated preinteraction expectations of efficacy caused by the anthropomorphic chatbot. Angry customers respond more punitively to these expectancy violations compared with nonangry customers.The decision to anthropomorphize a chatbot is a deliberate and strategic choice made by the firm. The current research shows that this choice has a significant impact on key marketing outcomes for a substantial (and increasing due to the pandemic; [57]; [60]) group of customers: specifically, those who are angry during the service encounter. As such, firms should attempt to gauge whether a customer is angry either before or early in the conversation (e.g., using NLP) and deploy a chatbot with an appropriate level of anthropomorphism or lack thereof. A less precise solution would be to assign nonanthropomorphic chatbots to customer service roles that tend to involve angry customers (e.g., customer complaint centers) while continuing to employ anthropomorphic agents in more neutral or promotion-oriented settings (e.g., searches for product information) due to their previously documented beneficial effects ([27]; [72]) and the current empirical evidence (i.e., Study 1, Web Appendix I). This strategic deployment of chatbots should help firms deliver better chatbot-mediated service experiences. Moreover, appropriate chatbot deployment can improve immediate customer satisfaction, company evaluations, and future purchase intentions (e.g., customer retention).Given our finding that the negative effect of anthropomorphism for angry customers is driven by an expectancy violation due to inflated preinteraction efficacy expectations, another practical implication is that marketers should consider how to frame their customer service chatbots to customers. As our final study shows, if an anthropomorphic chatbot is deployed to angry customers, it is best to downplay its capabilities. Some companies seem to have intuited this, as illustrated by the aforementioned Slack bot example. Similarly, the Poncho weather app told people, ""I'm good at talking about the weather. Other stuff, not so good"" ([65]). Explicitly informing customers that they are conversing with an imperfect chatbot lowers preinteraction efficacy expectations that were inflated by anthropomorphic traits. Yet, this is not obvious to all companies; there are plenty of examples of chatbots that inadvertently increase preinteraction efficacy expectations. For example, Madi, Madison Reed's chatbot, is labeled a ""genius"" ([49]) and Tinka, T-Mobile's chatbot, is given a 18,456 IQ ([47]). Of course, Study 3 shows that meeting the high expectations for service can also reduce the negative impact of anthropomorphism. Thus, by utilizing these strategies, all customers can be handled well via AI technology.Alternatively, firms could transfer angry customers directly to a real live person to assist them, thus avoiding an anthropomorphic chatbot–based expectancy violation entirely. Yet this option incurs additional costs and assumes that the human agent has greater agency and efficacy. While it is plausible that human agents will deliver higher quality service, in actuality, human agents suffer from constraints that limit their effectiveness. Thus, future research might address how people respond to chatbots compared with humans. It would be interesting to explore whether higher expectations of quality and agency would be compensated for by social norms of polite interactions and compassion for others.In addition, anger may not be the only relevant emotion to consider managerially or theoretically. While our data indicate that anger is of primary importance and is the most commonly identified emotion in service contexts, it is possible that with more sophisticated language processing tools, other emotions, different sources of those emotions, and social conventions could become relevant. For example, it could be that anger remains relevant in customer service contexts but that the source of the anger, such as whether it arises from a lack of procedural or interactional fairness, also impacts the success of anthropomorphic bots ([ 7]). Thus, it is important for future research to continue to investigate how the complexities of emotion, sources of emotion, and social norms interact to influence the effectiveness of anthropomorphic digital customer service agents.It is worth noting that there might be a point in the future when the conversational performance of AI becomes sufficiently advanced and its implementation so commonplace that expectancy violations simply cease to be a concern. In this future, chatbots might be capable of greater freedom of action, in addition to performing intuitive and empathetic tasks ([33]). In approaching such a point, the difference between the reactions of angry and nonangry customers would likely diminish until the groups are nondistinct, and anthropomorphism might cease to conditionally influence customer outcomes. However, this future does not appear to be imminent ([58]).In the short and medium term, therefore, as firms experiment with conversational agents in a variety of customer-facing roles, it remains important to consider the anthropomorphic traits of the chatbot, including simple features such as naming (i.e., ""Alexa""), language style, and the degree of embodiment, along with the specific customer contexts in which the interactions are likely to occur. Specific contexts can vary from traditional corporations to government (e.g., the Australian government chatbot), law (e.g., the ""DoNotPay"" chatbot), and psychotherapy (e.g., the ""Woebot"" chatbot). It is worthwhile to decide, and important for future research to explore, which chatbot is most appropriate for any given interaction, according to the chatbot's characteristics and the specific context.Altogether, chatbots deliver a multitude of benefits to the business (e.g., scalability, cost reductions, control over the quality of interactions, additional customer data). As such, they will continue to be a valuable tool for marketers as the technology matures. Here, we have shown that the unconditional deployment of humanized chatbots leads to negative marketing outcomes, from dissatisfaction to lowered purchase intentions. However, with careful and conscientious implementation, considering the customer's emotional state (e.g., anger), firms can reap the benefit of this burgeoning technology. " 7,"Buy Less, Buy Luxury: Understanding and Overcoming Product Durability Neglect for Sustainable Consumption"," The authors propose that purchasing luxury can be a unique means to engage in sustainable consumption because high-end products are particularly durable. Six studies examine the sustainability of high-end products, investigate consumers' decision making when considering high-end versus ordinary goods, and identify effective marketing strategies to emphasize product durability, an important and valued dimension of sustainable consumption. Real-world data on new and secondhand accessories demonstrate that high-end goods can be more sustainable than mid-range products because they have a longer life cycle. Furthermore, consumers engage in more sustainable behaviors with high-end goods, owning them for longer and disposing of them in more environmentally friendly manners. Nevertheless, many consumers prefer to concentrate their budget on multiple ordinary goods in lieu of fewer high-end products partly because of product durability neglect, a failure to consider how long a product will last. Although consumers generally believe that high-end products last longer, they fail to take such a notion into account when making purchases. Finally, this research offers actionable strategies for marketers to help consumers overcome product durability neglect and nudge them toward concentrating their budget on fewer high-end, durable products.","The proof that you did something good is the fact that you can use it again and again.—Miuccia Prada, head designer of Prada ([57])Luxury and sustainability are one and the same.—François-Henri Pinault, chief executive officer of Kering ([61])The rise of fast-fashion retailers such as H&M and Zara has enabled consumers to increasingly adopt a habit of buying disposable clothing and accessories. More than half of fast-fashion products are worn for less than a year, contributing to a 36% decrease in the average number of times an item is worn compared with 15 years ago ([20]). Although fast fashion offers consumers access to trendy, albeit short-lived, attire at affordable prices, it also exacts high environmental costs, not only in the production phase but also in the postproduction stages of use and disposal. Indeed, the fashion industry has become one of the largest polluters ([31]), contributing to 10% of global carbon emissions as well as 20% of global wastewater ([69]).Faced with this reality, several trends have emerged over the past decade to counterbalance fast fashion. Notable examples include the rise of sustainable luxury consumption ([ 2]), the concepts of ""buy less, buy better"" ([13]) and ""slow-fashion"" ([60]), and the trend of celebrities wearing identical outfits at multiple ceremonies ([10]). Consumers advocating such lifestyles strive to purchase fewer, higher-end products that will last longer, rather than many inexpensive products that will be quickly thrown away. However, these trends and movements still represent niche segments, as products with expensive price tags do not fit the stereotype of sustainable consumption generally associated with restraint and moderation ([ 5]).Focusing on the clothing and accessories industries, this research explores three aspects of sustainable luxury consumption: ( 1) whether high-end[ 6] products are more sustainable by virtue of their longer product life cycles, ( 2) how consumers process information regarding the durability of these high-end products, and ( 3) how marketers can help consumers overcome a failure to consider product durability and promote the purchase of fewer, higher-end products that will last longer.Across six studies, including one in which we examine real-world data on new and secondhand shoes and bags, we demonstrate that high-end goods can be more sustainable than ordinary products because of their longer life span and environmentally friendly ways in which they are disposed of. Yet we find that many consumers prefer to allocate the same budget on multiple lower-end products instead of purchasing fewer, higher-end products. We show that these preferences are due to product durability neglect, a failure to consider how long a product will last. In addition to deepening the theoretical understanding of durability as an important dimension of sustainable consumption ([35]; [48]; [71]), the present research also provides actionable strategies for marketers of high-end brands to emphasize the durability of their products and, thus, nudge consumers toward a more sustainable world with fewer, higher-end products that last longer. Given that the clothing and accessories industries are among the top-polluting businesses ([31]), the present work focuses on apparel goods (e.g., shoes, bags, clothes); however, as we elaborate in the ""General Discussion"" section, the insights from this research can be applied to many other industries as well. Theoretical Background and Hypotheses Durability as a Dimension of Sustainable ConsumptionIn general, sustainability in consumption refers to ""the consumption of goods and services that meet basic needs and quality of life without jeopardizing the needs of future generations"" ([52]). Building on prior work in operations and marketing that addresses sustainability from various stages of the product cycle ([15]; [64]), our conceptualization identifies three key dimensions of sustainability: ( 1) sourcing of materials in the supply chain; ( 2) production and manufacturing processes, including labor practices; and ( 3) durability and life span of products, including use and disposal.We focus on the third dimension of sustainability: product durability and life span. This dimension has mostly been overlooked, with a vast amount of research on sustainability focused on the first two dimensions related to the sourcing of raw materials and the manufacturing processes (for a review, see [71]]). Consistent with extant literature that identifies both the functional and stylistic elements of durability ([14]; [46]), we define a product as durable if it provides extended functional benefits (e.g., it does not deteriorate after a few washes in the case of apparel goods), as well as stylistic benefits (e.g., it does not quickly go out of style, reflecting its timelessness).Product durability not only contributes to less waste production, but also offers tangible benefits to both consumers and companies. First, given that consumers not only want to be sustainable but also be mindful of personal financial resources ([35]), they can achieve both by selectively purchasing fewer products. By extending the life span of their purchases (i.e., using selectively purchased products for longer duration, and reselling or donating them), consumers can make strategic use of their financial budget, while actively participating in the sustainability movement. The online retailer [21] underscores these benefits when promoting its ""Second Life"" consignment service, proposing that ""by selling your pre-loved bag, you're extending its life and helping the environment.""Second, product durability can benefit companies as well: it is a timely attribute from a managerial standpoint that is highly consistent with the aforementioned trends of sustainable luxury and ""slow-fashion"" that have gained traction in recent years ([13]). In fact, many high-end entrepreneurial brands, such as Pivotte, Everlane, and Cuyana, as well as more established premium and luxury brands, such as Patagonia, Brunello Cucinelli, and Loro Piana, promote the use of high-quality, durable materials that reduce downstream environmental impact while online luxury retailers like Net-a-Porter allow shoppers to filter the products by their sustainability (for examples, see Web Appendix W1). Given that some consumers purchase more expensive green products to signal status ([34]), promoting the durability of the product can be an appealing strategy for high-end brands to promote not only the luxuriousness of their products, but also the sustainable nature of their goods. Thus, we propose that encouraging the purchase of fewer, high-end durable products can be a win for both consumers and companies. Durability as a Dimension of LuxuryLuxury products not only embody high prestige and rarity, but also entail longer life spans and durability ([39]; [72]). More specifically, we conceptualize luxury in line with [72], which proposes that luxury goods score high on the following four dimensions: financial dimension (e.g., price, resale price), functional dimension (e.g., durability, quality, reliability), individual dimension (e.g., hedonism, self-identity), and social dimension (e.g., conspicuousness, status signaling). Thus, durability—both its functional and stylistic elements—is central to the definition of luxury ([ 2]; [ 4]). Given that sustainable consumption and luxury overlap on the product durability dimension, we argue that the consumption of fewer, high-end goods can be an effective means to engage in sustainability. Product Durability NeglectAlthough both extant literature and industry reports reveal that luxury products and sustainability share some common traits, such as durability, many consumers disregard the sustainable nature of high-end products ([ 5]). In fact, we propose that consumers may outright neglect product durability when contemplating high-end purchases because durability is not a salient attribute when considering these products. Such overlooking is consistent with prior work demonstrating that consumers are prone to making decisions based on easily accessible cues and background context ([22]; [68]) and often fail to consider attributes that are not readily salient ([45]). For instance, when consumers choose between two stereo systems, they may focus on comparing readily available attributes, such as price and the technical specifications (e.g., watt per channel) while neglecting nonsalient, yet important, opportunity costs considerations ([25]). Our product durability neglect hypothesis is also related to prior work showing that consumers disregard the frequency of usage when contemplating purchases of various appliances (e.g., microwaves, monitors, phones) because such information is not readily available ([26]; [29]; [50]).Although previous work has explored various neglect biases, none has directly considered product durability. We propose that when consumers think of high-end luxury apparel, product durability may not be readily salient because they imagine other, more exemplary instances of luxury consumption (e.g., wearing high-end clothing for status signaling, splurging on a particular item for indulgence). In other words, high-end products are particularly susceptible to product durability neglect because consumers spontaneously focus more on the individual (e.g., hedonism, self-identity) and social (e.g., conspicuousness, status signaling) aspects of luxury goods ([41]; [72]). Accordingly, when choosing between different options, thinking of such prototypical occurrences related to high-end goods may crowd out consumers' ability to consider the relatively longer-lasting nature of these products in the consideration set. This theorizing is also consistent with the accessibility-diagnosticity model ([49]) and the scope insensitivity bias ([12]), suggesting that the accessibility of a given input (e.g., the associations of high-end products with hedonism and status signaling) increases the likelihood that such input will be used to form judgments.Therefore, we predict that, even when holding the total spending and the time horizon constant, consumers considering different product options will prefer to spend their budget on multiple ordinary items in lieu of fewer, high-end goods because, at least in part, they neglect product durability. More formally, we hypothesize: H1: Holding the total budget and time horizon of consumption constant, consumers prefer to purchase multiple mid-range products over fewer high-end products. H2: The effect specified in H1 is mediated by product durability neglect. Marketing Durability for a Better WorldWith growing concerns about environment preservation, many luxury brands are increasingly embracing sustainability. Executives at leading luxury brands and conglomerates, such as LVMH Louis Vuitton Moët Hennessy and Kering, have announced initiatives to make sustainability and the production of sustainable luxury products a top priority ([42]; [59]). We propose that focusing on the durability aspect of sustainability can be an effective marketing strategy for high-end brands to promote their products, while at the same time nudging consumers toward buying fewer, better goods. That is, emphasizing product durability may shape consumers' actual purchase behavior while promoting an attribute central to luxury brands.Work by behavioral economists and marketing researchers on nudging and choice architecture has found that careful message framing and product positioning can be an effective intervention to prompt behavioral change ([44]; [65]). With specific regards to product choices, making an overlooked attribute more salient or emphasizing explicit cues can help individuals overcome their neglect of various product attributes or decision factors ([25]; [50]). For example, explicitly stating that buying a cheaper stereo system will leave more money available for other purchases helps consumers overcome opportunity cost neglect ([25]). Accordingly, we predict that making product durability salient when choosing among different options will nudge consumers toward selecting fewer high-end products over multiple ordinary ones. More formally, we hypothesize the following: H3: Increasing the salience of product durability encourages the choice of fewer high-end products over multiple mid-range products. Overview of StudiesWith real-world evidence grounded in actual consumption contexts and responses from real product owners, Studies 1 and 2 demonstrate that high-end products can be sustainable because they have longer life spans. In particular, Study 1 provides empirical evidence from the web, with data from over 4,600 new and secondhand shoes and handbags scraped from online stores, and demonstrates that high-end goods are more sustainable than mass-market goods because they are more likely to be sold again as secondhand products. Study 2 finds that consumers engage in more sustainable behaviors with high-end goods (vs. low-end goods), as they desire to keep these items for a longer duration and engage in sustainable behaviors after use (i.e., resell or donate the products) instead of disposing of them. Despite the sustainable nature of high-end goods, Studies 3 and 4 demonstrate that consumers prefer to buy multiple ordinary items over fewer high-end items because, at least in part, they fail to consider the durability of the high-end products. Complementing these findings, the last set of studies also explores the managerial implications of the present research for marketers. Specifically, Study 4 identifies an effective strategy for marketers of high-end products to make durability salient and encourage the sustainable consumption of durable products. Finally, Studies 5a and 5b examine consumers' revealed preferences in two choice-based conjoint surveys, one of which was conducted in collaboration with a clothing company (Pivotte). When consumers have to consider durability and cannot neglect it by design, our results show that they do value durability as an important product attribute relative to other attributes, such as price and style (Study 5a), and that durability can be marketed as a valuable dimension of sustainability (Study 5b). Study 1: The Prevalence of High-End Goods on Secondhand MarketsThe objective of the preregistered Study 1 is to provide evidence in favor of the premise that high-end goods can be more sustainable than ordinary goods because they are more durable. To this end, we collect data on more than 4,600 secondhand and new products sold online and examine the presence of luxury products in secondhand markets. In line with our proposition that high-end goods are more durable than ordinary products, we expect to observe a prevalence of high-end brands on websites for secondhand products. MethodThe preregistration detailing the methods and the analysis is available at https://aspredicted.org/blind.php?x=uj7k8h. To acquire relevant data in an objective manner, we identified the most frequently searched online retailers of clothing accessible to U.S. consumers through organic results on Google Search. Next, we constructed a list of the top 20 online retail stores for secondhand products and new products (for a detailed description of the methods, see Web Appendix W2). The top retailers for secondhand products based on the total tallied count were eBay, Grailed, Poshmark, Swap, The RealReal, thredUP, Tradesy, Vestiaire Collective, and Vinted. The top retailers for new products were Anthropologie, Boohoo, Charlotte Russe, Macys, MissGuided, NastyGal, Nordstrom, Target, Walmart, Zappos, and Zaful. Given that some retailers of new clothes only listed a small number of items, we scraped for information from a slightly larger number of retailers selling new clothes (11) than secondhand retailers ( 9) to have a similar number of items collected for each type of apparel (i.e., at least 2,000 products for each category). Moreover, to provide a more conservative test of our hypotheses, we wanted to perform robustness analyses in the absence of products from Target and Walmart (two retailers known for their affordable products) and have the same number of retailers in each list.After we compiled the list of retailers, automated web crawler scripts scraped information from the 20 websites on both shoes for men and women, and handbags for women. We selected these categories given our focus on apparel and accessories. For each product, we collected the following information (if available): current price, original price, brand name, and detailed product category (e.g., kitten heels). For each website, the crawler collected information on the first 100 available products listed in men's shoes, women's shoes, and women's handbags categories. If a particular retailer listed fewer than 100 products or did not have a specific category of goods (e.g., did not sell handbags), information on all available products was collected. Web Appendix W3 reports summary statistics on the total number of items scraped, organized by product category and type. We collapse the data for shoes and bags for ease of exposition and report the pooled results below; analyzing data by separate product categories does not change the results (all reported in Web Appendix W4). ResultsWe collected data for 4,694 secondhand and new shoes and bags from 812 brands. To test our prediction that high-end goods are more prevalent on secondhand retailers than in new product retailers, we asked 1,800 Amazon Mechanical Turk (MTurk) respondents from the United States (60% female; Mage = 37.4 years) to classify the brands of the scraped products as high-end, mid-end, or low-end (or unfamiliar, if they did not know the brand). Each participant rated a random set of 20 brands; we converted the ratings into a numerical brand status score by assigning high-end a value of 3, mid-end a value of 2, and low-end a value of 1. Of the 812 brands, we constructed status scores for 268 brands based on respondents' familiarity with the brands, leading to a total of 2,990 ratings.To test the prevalence of high-end branded products on secondhand markets, we examined the average status scores of the brands in the new and secondhand product categories. As we predicted, the respondents perceived the average status of the brands listed on secondhand retailers as higher-end than those listed on new product retailers (M2ndhand = 2.47 vs. Mnew = 2.05; t( 2,988) = 28.90, p <.001, d = 1.06). The difference was also significant without Target and Walmart (M2ndhand = 2.47 vs. Mnew = 2.09; t( 2,658) = 24.07, p <.001, d =.94). As an additional test, we confirm that respondents perceived the brands listed on the secondhand websites as higher-end than the midpoint ( 2) of the high/low scale (M2ndhand = 2.47; t( 1,429) = 41.62, p <.001, d = 1.10).To examine these results at a more granular level and test the robustness of our prediction, we also evaluated the average status scores by percentiles of price (Web Appendix W5). Specifically, we observed that the average status of secondhand branded products was higher than the average status of new products across different percentiles of price. Thus, the significant difference in the average status scores of the secondhand and new products was not simply driven by the large differences in the extreme ends of the data set (i.e., differences in a small number of the most and least expensive items for these secondhand and new products). Consistent with our prediction, the results indicate that secondhand products had higher status than new products across all price points.The average price for new shoes and bags was $247.28 (SD = $506.71), and for secondhand shoes and bags was $92.64 (SD = $189.91). Because the price distribution was skewed to the right, we logged the price to deal with outliers: the average logged price for new products was 1.68 (SD =.44) and for secondhand products was 2.01 (SD =.59). As expected, the products from secondhand retailers were listed at higher prices than those from new product retailers (M2ndhand = 2.01 vs. Mnew = 1.68; t( 4,692) = 22.02, p <.001, d =.65). The difference was also significant without Target and Walmart (M2ndhand = 2.01 vs. Mnew = 1.76; t( 4,092) = 15.60, p <.001, d =.49; for additional robustness checks, see Web Appendix W6). Alternative explanationsAncillary analyses cast doubt on several alternative explanations. One might wonder whether these results could be driven by secondhand products being unique or having better aesthetics, leading to a higher average brand status and price relative to the new products. To rule out these possibilities, we scraped the photos of ten products from each category from each of the 20 websites, for a total of 500 product images. Then, we recruited 1,000 U.S. respondents (74% female; Mage = 34.5 years) on MTurk to rate these product images on uniqueness and liking. Specifically, each respondent looked at two randomly chosen product images and answered the following questions for each product on a seven-point Likert scale: ( 1) ""How unique does the product look to you?"" (1 = ""Not unique at all"" to 7 = ""Very unique [one-of-a-kind]"") and ( 2) ""How much do you like the design of the product?"" (1 = ""Do not like at all"" to 7 = ""Like it very much""). The new and secondhand products were rated similarly in terms of uniqueness (Mnew = 4.75 vs. M2ndhand = 4.75; t(498) =.00, n.s.). The respondents liked the new products more than the secondhand products (Mnew = 4.37 vs. M2ndhand = 4.06; t(498) = 2.35, p =.019, d =.21), which was opposite of what the results would have been had the alternative account been at play. Importantly, controlling for these factors by conducting an analysis of variance with average brand status scores as the dependent variable, product type as the main factor, and uniqueness and liking ratings as two covariates revealed that product type (new vs. secondhand) was the only significant factor (F( 1, 319) = 95.78, p <.001, η2 =.23), whereas the two covariates had no significant effect (uniqueness: F( 1, 319) =.02, n.s.; liking: F( 1, 319) = 3.58, n.s.).[ 7] DiscussionBy directly scraping field data from 20 retailers selling secondhand products, our preregistered Study 1 provides correlational support for the notion that high-end products have a longer life cycle because they are more prevalent on online secondhand retailers than ordinary goods. One may wonder whether the presence of high-end goods on secondhand markets is a mere by-product of a higher starting original price. That is, perhaps more high-end products are listed on secondhand retailers just because they are more expensive. While this is a possibility, if high-end apparels were merely expensive but not long-lasting, our thesis that these high-end products are more sustainable by virtue of their durability would not be supported. On the contrary, the evidence stemming from this data set suggests that, in addition to possibly being more costly, high-end goods also last for a long time and make it to additional life cycles in the market. Study 2: Sustainability of Luxury GoodsTo find further support for the notion that high-end goods can be more sustainable than lower-end items because high-end products are used for more extended periods and are discarded in more environmentally friendly manners, we directly asked owners of high- and low-end accessories to provide information about some of their belongings. We predict that the more high-end an owned item is, the longer the intended duration of ownership, and the lower the intention to throw it away instead of engaging in sustainable disposal behaviors, such as reselling, donating, or giving away the product to someone else. In line with Study 1, we expect that high-end items will be more durable and discarded more sustainably than ordinary goods. MethodWe recruited 340 wealthy women from the United States on Qualtrics (Mage = 30.4 years; Mincome ≥ $121,000[ 8]) for an online study. We purposely recruited female respondents with high annual income to control for gender and financial background and to increase the likelihood that they would own products from diverse price ranges. We randomly assigned respondents to one of two between-subjects conditions (high-end vs. low-end) and asked them to provide information about both a pair of shoes and a bag that they owned (order counterbalanced). In the case of shoes, for example, respondents were told: ""Please think about a high-end[ 9] [low-end] pair of shoes that you own."" If they did not own any products that fit the description, respondents in the high-end condition thought of the most expensive products they owned, whereas those in the low-end condition thought of the least expensive products they owned: ""If you do not have any pair of high-end [low-end] shoes, please think about the most [least] expensive pair of shoes you own."" We used identical phrases to collect information about the respondents' bags.Then, respondents answered a series of questions about their owned products, including ( 1) purchase price (""How much did you pay for the pair of shoes/bag?""), ( 2) length of planned ownership (""How long do you plan on wearing your shoes/using your bag before you no longer want them [it]?"" on a seven-point Likert scale: 1 = ""0–6 months,"" 2 = ""6 months–1 year,"" 3 = ""1 year–1 year and 6 months,"" 4 = ""1 year and 6 months–2 years,"" 5 = ""2 years–2 years and 6 months,"" 6 = ""2 years and 6 months–3 years,"" and 7 = ""> 3 years–specify""), and ( 3) disposal (""What will you do with the pair of shoes/bag when you no longer want them/it?"" with the options ""sell it,"" ""give it to someone else,"" ""throw it away,"" ""donate it,"" ""keep it even though I will not wear it,"" and ""other–specify""). We recoded the disposal responses as a binary dependent variable depending on whether the answer was a sustainable behavior (1 if the respondent indicated selling it, giving it to someone else, donating it, or keeping it) or an unsustainable behavior (0 if the respondent indicated throwing it away). No value was assigned for ""other—specify"" (1% of responses). We also collected a series of ancillary variables on these products (e.g., physical product condition, who bought them). Controlling for all these variables in the analyses does not change the results. Results Price checkThe average price of shoes across the two conditions (high-end and low-end) was $183.67 (SD = $535.54). We found a significant difference between high-end and low-end conditions in purchase price of the owned shoes (Mhigh = $242.90 vs. Mlow = $127.17; F( 1, 338) = 4.00, p =.046, η2 =.01). The average price of bags was $264.18 (SD = $624.62). Similar to shoes, we found a significant difference between the two conditions in purchase price (Mhigh = $385.19 vs. Mlow = $148.74; F( 1, 338) = 12.59, p <.001, η2 =.04).The significant differences between the purchase prices of the high-end and low-end products confirm that respondents indeed thought of a high-end or a low-end pair of shoes and a bag depending on the condition to which they were randomly assigned (high-end vs. low-end). Note that the average prices for the low-end products were not trivial (e.g., $148.74 for ""low-end"" bags). This was likely a by-product of recruiting high-income respondents and provides a more stringent test of the durability of high-end products. Expected length of ownershipFor ease of exposition, we collapse the data for shoes and bags. However, all results are also significant when analyzing the two product categories separately. Consistent with our prediction, we found that the expected duration of ownership was significantly longer for the high-end products than the low-end products (Mhigh = 5.05 vs. Mlow = 4.13; F( 1, 678) = 39.74, p <.001, η2 =.06). DisposalAs predicted, there was a significant difference in the overall responses by condition (χ2( 1) = 17.77, p <.001, ϕ =.16). Specifically, owners of the high-end products displayed a greater willingness to engage in sustainable disposal behaviors (%high = 91.10) compared with the owners of the low-end products (%low = 79.54); the owners of the low-end products were more likely to throw away the products than the owners of the high-end products (%low = 20.46 vs. %high = 8.90). DiscussionStudy 2 provides further empirical support that high-end goods are more sustainable than low-end products because consumers who own high-end goods intend to own them for longer and dispose of them in more sustainable ways. One potential weakness of Study 2 could be that the owners of high-end products were motivated to justify their purchases and, thus, stated that they would use these products for longer. To address this possible issue of postpurchase justification, in the next studies, we ( 1) directly explore consumers' preferences between high-end and lower-end apparel before making a purchase and ( 2) test the premise that high-end goods last longer regardless of ownership status. The next two studies also directly test our proposed product durability neglect account. Study 3: Product Durability NeglectIn Study 3, we investigate whether consumers prefer multiple mid-range products over a high-end product (H1) because they neglect product durability (H2). The study aims to provide evidence on the process in two ways. First, building on established methods to detect neglect biases in research (e.g., [29]; [50]; [63]), we test whether product durability neglect underlies consumers' preferences toward relatively less sustainable product choices by examining respondents' thoughts as they decide between different options. Second, we assess whether consumers' differing intertemporal preferences make certain consumers more susceptible to product durability neglect than others. Given that the benefits of sustainable consumption are often realized over a long time horizon, those who are more patient and have a more future-oriented mindset tend to engage in more sustainable consumption behaviors compared with myopic consumers, who have a stronger present bias ([ 3]; [38]). In the case of product durability, consumers who have a more future-oriented mindset should recognize that durable products yield benefits in the future because these products have longer life spans. Thus, if product durability neglect is indeed at play, we expect consumers with relatively lower intertemporal discount rates ([24]) to favor fewer high-end products (vs. multiple mid-range products) compared with consumers with higher intertemporal discount rates. MethodWe recruited 201 U.S. respondents for a paid online survey on MTurk (44% female; Mage = 34.7 years). To increase the generalizability of our findings and confirm that our results are not driven by the specifics of the product category, we tested two products, different price points, and different time horizons. To this end, all respondents were randomly assigned to one of two between-subject replicates (product type: shoes vs. winter coat) and asked to make a purchase decision about shoes or winter coat. For shoes, respondents read, ""Imagine that you typically have a shoes budget of $400[10] per year. You have two options regarding how you want to spend the $400. Which would you prefer?"" Then, respondents selected either ""buy one high-end pair of shoes for $400"" or ""buy four mid-end pairs of shoes for $100 each"" (the order of appearance of the two options was randomized). Similarly, for winter coats, respondents read, ""Imagine that you have a winter coat budget of $2,000[11] for the next ten years. You have two options regarding how you want to spend the $2,000. Which would you prefer?"" Next, respondents chose either ""buy one high-end winter coat for $2,000 this year"" or ""buy one mid-end winter coat for $200 every year"" as their response (order of appearance randomized).Then, all respondents listed at least one and up to five thoughts about the decision that they just made about the shoes or the winter coats (""In the form below, please list at least one reason why you decided to choose that option""; open-ended). To assess the presence of durability-related content, we developed a corpus of words that contained the following durability-related roots: ""last"" and ""dura"" (allowing to detect relevant terms such as ""long-lasting,"" ""last,"" ""durability,"" and ""durable""). Then, we counted the number of times these key terms appeared in the comments using the function grepl() in R. For instance, if a particular respondent mentioned the word ""durable"" in a given comment, this was tallied once.Finally, all respondents completed the Dynamic Experiments for Estimating Preferences ([66]), which involved 12 rounds of adaptive questions related to one's time preferences (i.e., a choice between a smaller, immediate gain and a larger, later gain). The data were analyzed using a hierarchical Bayesian approach to estimate individual-level parameters in the quasihyperbolic time discounting model, including the estimates of beta, delta, and the discount rate r ([51]; [66]). Results ChoiceRegarding shoes, 78.85% of respondents preferred to buy multiple mid-range products, whereas only 21.15% of respondents preferred to buy one high-end product. Similarly, regarding winter coats, 76.29% indicated that they would prefer multiple mid-range products, whereas only 23.71% indicated that they would like one high-end product. As in previous studies, we collapse the two products—and report the results in aggregate (separate analyses of each category led to similarly significant results). Across the two products, 77.61% of respondents preferred to buy multiple mid-range products, whereas only 22.39% of respondents indicated that they would like to buy one high-end product. Thus, the majority of respondents preferred to consume multiple mid-range products (χ2( 1) = 61.30, p <.001, h = 1.17). Thoughts generatedRespondents generated a total of 647 comments, with an average of 3.22 thoughts per person. A two-sample t-test revealed no significant difference in the average number of thoughts generated between those who chose the high-end option and those who chose the mid-range option (Mhigh = 3.09 vs. Mmid = 3.26; t(199) =.65, n.s.). Only 6.96% of all comments containing durability-related content, regardless of their product choice. However, a two-proportions z-test revealed that a significantly higher proportion of respondents who chose the high-end option mentioned durability in their thoughts (%high = 14.39) compared with respondents who chose the mid-range option (%mid = 4.92, χ2( 1) = 13.69, p <.001, h =.33). In support of our predictions, these results suggest that those who chose to allocate their budget on multiple mid-range products neglected product durability to a greater extent. In contrast, durability considerations were relatively more accessible for those who opted to concentrate their budget on one high-end option. Intertemporal preferencesTo test our account through intertemporal preferences, we ran a logistic regression with choice as the dependent variable (coded as 1 for choice of one high-end product and as 0 for choice of multiple mid-range products), discount rate r as the predictor, and product type (shoes vs. winter coat) as a covariate. The discount rate r was a negative and significant predictor of choice (β = −100.01, χ2( 1) = 4.96, p =.026). As expected, respondents with a lower discount rate were more likely to choose the high-end option instead of the ordinary options. The product type did not predict choice (β = −.11, χ2( 1) =.11, n.s.). Given that the higher discounting rate r indicates a greater present bias, and less patience, these results are consistent with product durability neglect and demonstrate that having a present-bias may impede consumers in recognizing the value of durability. ReplicationTo increase statistical conclusion validity ([49]), we replicated the main findings in another study involving 248 respondents (33% female; Mage = 19.5 years; see Web Appendix W7) recruited at the behavioral lab of a U.S. university. Follow-up studyAlthough the lack of durability-related content in respondents' open comments suggests that consumers neglect product durability, it is possible that instead of neglecting product durability, consumers simply do not believe that high-end products are more durable and, thus, are reluctant to choose them. To address this possibility, we recruited 200 respondents in the lab at a U.S. university (57% female; Mage = 19.5 years) and asked them to rate, between-subjects, the durability of a high-end or a mid-range pair of shoes. If the alternative account—that consumers are doubtful that high-end products can be more durable—were supported, we would find no significant differences in the life span estimates of the high- and mid-range products. Our results go against such an account: respondents indicated that the high-end item would last for a significantly longer time than the mid-range item, in support of the lay belief that high-end products are more durable (Mhigh = 4.84 vs. Mmid = 3.05; t(198) = 7.48, p <.001, d = 1.06; see Web Appendix W8). DiscussionStudy 3 demonstrates that when presented with two options, most respondents preferred to spend the same amount of money on multiple ordinary goods instead of on one high-end good (H1) because, at least in part, they did not consider the durability of the high-end product (H2). Consistent with our account, product durability neglect was stronger for respondents who chose multiple mid-range products (vs. one high-end product). Moreover, those who had a higher discount rate r tended to prefer multiple mid-range products.Although these results support our product durability neglect hypothesis, there remain other potential alternative accounts. For instance, it is possible that the respondents opting for multiple goods, in addition to neglecting durability, were also driven by variety-seeking motives or risk aversion. It is also conceivable that the respondents opting for high-end goods may have mentioned durability for self-presentation motives ([23]) or as a justification for choosing a more indulgent product ([43]). Because these motives may be concurrently at play, the next study shows more unequivocally that product durability neglect underlies part of the observed effects by experimentally manipulating the salience of durability in a marketing-relevant context. Study 4: Nudging Product Durability for a Better WorldThe purpose of Study 4 is twofold. First, consistent with previous research on neglect biases ([25]), we manipulate the salience of durability to further establish product durability neglect as the process underlying the preference for multiple mid-range products (vs. fewer high-end products). In doing so, we also control for potential alternative explanations such as variety seeking. Second, we explore the effectiveness of a marketing-relevant intervention to nudge consumers toward more durable products using realistic stimuli embedded in online product pages. MethodThe preregistration detailing the methods and the analysis is available at https://aspredicted.org/blind.php?x=yy6z3y. We recruited 421 U.S. respondents (51% female; Mage = 32.2 years) on Prolific Academic for a paid online survey. We randomly assigned respondents to one of two conditions between-subjects (control vs. durability). Respondents considered two product pages—one for a high-end item priced at $80 and another for a mid-range item priced at $20[12]—featuring a black sweater sold by two fictitious brands, ""Luyana"" and ""Cooper.""We opted for fictitious brand names to control for preexisting brand associations with well-established brands ([ 8]). To rule out potentially confounding effects of different models, styles, and brand names used in the stimuli, we created two versions—A and B—of the ad for all the conditions described next. In one version, a particular model, style, and brand name, ""Cooper,"" was used in the high-end condition. In another version, another model, style, and brand name, ""Luyana,"" was used in the high-end condition. This design serves as a between-subjects replicate, and we expect to observe the predicted results for both versions of the stimuli. In addition, to account for variety seeking, we embedded the focal product in a product page featuring three different colors (i.e., black, pink, and camel) to prime the notion that one could opt for multiple items of various colors. We also priced the items so that one could opt for several ordinary products with the same budget of one high-end item. Finally, we matched respondents' gender to the gender of the model featured to increase relevance. For ease of exposition, we report stimuli and results consistent with version A, in which Luyana was the mid-range retailer and Cooper was the high-end retailer.All respondents read the following information about the two retailers: ""Luyana is a retailer that offers mid-range clothing. Luyana typically sells sweaters priced around $10–$20. Cooper is a retailer that offers high-end clothing. Cooper typically sells sweaters priced around $70–$80."" Then, they saw two product pages, each with an ad copy promoting the products. In the control condition, the high-end option read, ""A high-end sweater with long sleeves, and ribbing at neckline and hem."" The mid-range option read, ""A mid-range sweater with long sleeves, and ribbing at neckline and hem."" In the durability condition, the high-end option read, ""A high-end, durable sweater. You can think of this sweater as a one-time purchase in one product that will last for many years"" (see Web Appendix W15 for a complete set of the stimuli).[13] The mid-range option read the same as in the control condition. Then, to check whether our manipulation increased the salience of durability and to ensure that respondents were actually paying attention, we asked, ""In the box below, please type about 2–3 keywords from the webpage above.""On the next page, all respondents read, ""Imagine that this year, you have a clothing budget of $80 to spend on sweaters. You have two options regarding how you want to spend the $80."" Then, respondents saw the following two options, buying ""one high-end sweater for $80 at Cooper"" or buying ""four mid-range sweaters for $20 each at Luyana,"" and were asked, ""Which would you prefer?"" As in Study 3, all respondents listed at least one and up to five thoughts about the choice that they just made and we counted the number of times durability-related terms appeared in the comments. Manipulation checkConfirming the success of the durability salience manipulation, an analysis of the keywords that the respondents wrote down as they were looking at the two images (i.e., an ad for Cooper and an ad for Luyana) revealed that those in the durability condition mentioned durability-related words (%durability = 42.28) more than those in the control condition (%control = 0, χ2( 1) = 223.19, p <.001, h = 1.42). ResultsWe ran a logistic regression with choice as the dependent variable (coded as 1 for choice of one high-end product and as 0 for choice of multiple mid-range products) and with condition (control vs. durability) and version (A vs. B) as the independent variables. As predicted, respondents chose the high-end option significantly more in the durability condition than in the control condition (%durability = 27.36 vs. %control = 15.79, β =.70, χ2( 1) = 8.14, p =.004). Importantly, we observed the predicted effect of the durability manipulation even when variety seeking is potentially at play (given the three colors and the possibility of buying up to four items with the same budget). Although not central to our hypothesis, there also was a significant effect of version such that respondents were more likely to choose the high-end option for the brand and style of Cooper (%A = 25.59 vs. %B = 17.62, β = −.48, χ2( 1) = 3.91, p =.048).[14]Respondents generated a total of 1,209 thoughts, with an average of 2.87 thoughts generated per person. A two-sample t-test revealed no significant difference between the average number of thoughts generated by those who chose the high-end option and those who chose the mid-range options (Mhigh = 2.97 vs. Mmid = 2.85; t(419) =.84, n.s.). Replicating results from Study 3, the vast majority of respondents, regardless of their product choice, did not mention any durability-related content in their thoughts, with only 7.28% of all comments containing such content. At the same time, a two-proportions z-test revealed that the magnitude of neglect was higher for those opting for multiple mid-range products (3.41% of all comments related to durability) over those choosing the high-end product (20.74%, χ2( 1) = 90.80, p <.001, h =.57). Mediation analysisWe performed a mediation analysis (PROCESS Model 4, [36]) with choice as the dependent variable, condition (control vs. durability) as the independent variable, and the number of durability-related thoughts generated as the mediator. As predicted, the extent to which a consumer chose the high-end option was mediated by the number of durability-related thoughts generated (indirect effect =.64; 95% confidence interval [CI95%] = [.41,.94]). DiscussionBy manipulating the salience of product durability, preregistered Study 4 provides additional support for the underlying process of product durability neglect and offers an effective strategy in online communication to promote high-end products. The findings suggest that making product durability more salient by mentioning the word ""durable"" is an effective and actionable intervention to encourage the sustainable consumption of fewer, better goods. Studies 5a and b: The Importance of Durability and How to Promote ItStudies 3 and 4 demonstrate that consumers tend to neglect product durability unless this attribute is made salient. However, even when durability is brought to consumers' attention, some important questions remain for marketers: Do consumers neglect durability because it is not on their radar at the time of purchase or because it is actually irrelevant to their product choice? Study 4 provides some evidence in favor of the former, but how much do consumers value durability relative to other important product attributes, such as price or design? And with specific regard to sustainability, can durability be legitimately framed as an aspect of sustainability?Conjoint analysis is particularly suitable for answering these questions. By including durability as one of the product attributes (Study 5a) or as one of the levels (Study 5b) in the design of the study, respondents cannot neglect durability and are forced to make trade-offs revealing their true preferences with regard to this particular product dimension. In other words, we explore how much consumers value durability relative to other product features when they are forced to consider it.In addition, in these studies, we further investigate managerially relevant ways to emphasize durability. In Study 5a, we frame durability as a standalone product attribute, independent from sustainability, enabling us to understand how consumers value different levels of durability when they are made concrete (e.g., a product that lasts five years vs. ten years). Further, we are able to understand the value of durability, compared with other attributes such as price, style, and the dimensions of sustainability (i.e., sourcing and manufacturing). In Study 5b (in collaboration with Pivotte, a U.S.-based clothing company), we explicitly frame durability as a dimension of sustainability, enabling us to determine whether durability can effectively be positioned as an aspect of sustainability. Taken together, Study 5a sheds light on how durability framings can appeal to a broader segment of consumers, independent of sustainability messaging and Study 5b demonstrates how durability can be positioned as a dimension of sustainability and used to target a specific segment of green consumers. Method: Study 5aWe recruited 162 (41% female; Mage = 27.8 years) graduate students at a U.S. university who completed the survey for course credit. To evaluate consumers' revealed preferences regarding durability with explicit trade-offs relative to other important product attributes (e.g., price, style), we employed a choice-based conjoint (CBC) survey using Sawtooth Software. We chose Moncler coats as the stimuli for this study given that Moncler was a popular, desirable high-end brand among the sample population (35% of respondents reported that they owned at least one Moncler product or expressed a desire to buy one in the future; 61% had heard of the brand before).We created a CBC survey with five attributes—price, style, color, durability, and sustainability—with three levels within each attribute. The durability attribute had the following three levels: low-level (""The textile used to make the coat will last about 5 years""), mid-level (""The textile used to make the coat will last about 10 years""), and high-level (""The textile used to make the coat will last about 15 years""). Importantly, with this configuration of attributes, we made the durability information explicitly concrete to emphasize the total life span (i.e., 5 years, 10 years, and 15 years). In addition, the sustainability attribute entailed the following three levels: the sourcing of materials (""Made with down feather meeting strict Down Integrity System and Traceability [D.I.S.T.] requirements for animal welfare""), the production process (""Manufactured at Fair Trade Certified™ facilities with fair wage and labor practices""), and use and disposal (""Certified to meet bluesign® criteria for advanced waste-reduction technologies to minimize carbon footprint after disposal""; for a full description of all the other attributes and levels, see Web Appendix W9).Each respondent completed 12 choices in random order and chose the most preferred option out of three Moncler coats based on their price, style, color, durability, and sustainability. To generate the choice sets, we used a full profile, complete enumeration design, producing the most orthogonal design for each respondent with respect to the main effects. After the choice task, we collected measures regarding awareness (""Have you ever heard of the brand, Moncler, before?""; yes/no) and ownership (""Do you currently own any Moncler coat(s) or have you ever considered purchasing one?"" with options ""No, I don't own and I don't plan on owning any Moncler coats,"" ""I currently don't own a Moncler coat, but I'm thinking of purchasing one,"" and ""Yes, I do own Moncler coat(s). Please indicate how many.""). Controlling for these factors does not impact the significance of the following results.We used Sawtooth's HB-Reg Module, which estimates a hierarchical random coefficients model, to calculate part-worth utilities of different attributes, a widely used approach in marketing research ([11]). We followed the approach outlined by [54] to computed the degree of confidence with which an attribute level is preferred to another attribute level (for calculations, see Web Appendix W10). Results: Study 5aFocusing on durability, we found significant differences among the part-worth utilities of each level from low-level (Mutility = −1.74), to mid-level (Mutility =.55) to high-level (Mutility = 1.19) durability. The mid- and high-levels of durability were preferred to the low-level with 100% confidence. The high level of durability was preferred to the mid-level with 99.84% confidence. Thus, respondents significantly preferred higher levels of durability compared with lower levels. For ease of interpretation, we also present the increase in part-worth utility from one level of durability to another in monetary ($) terms.[15] An increase from the low-level (5 years) to the mid-level (10 years) of durability equates to an increase of $296.35 in the value of a product. Similarly, an increase from the mid-level (10 years) to the high-level (15 years) equates to an increase of $76.97 in the value of a product (for calculations, see Web Appendix W11).Looking at product profiles holistically, the relative importance weights indicated that style was the most important attribute (43.94%; CI95% = [40.65, 47.22]). As Figure 1 shows, price (21.59%; CI95% = [19.31, 23.87]) and durability (18.87%; CI95% = [16.96, 20.79]) were the second-most important attributes and did not significantly differ from each other. Finally, color (10.09%; CI95% = [8.27, 11.92]) and sustainability (5.51%; CI95% = [4.88, 6.13]) were the least important attributes. Overall, these results indicate that, when respondents were obliged to consider it, the durability of the textile was as important as price. Thus, durability emerged as a key factor in respondents' purchase decisions that was second only to style. In contrast, the sustainability of the product was not a particularly important attribute, and significantly less important than durability as a standalone attribute.Graph: Figure 1. Study 5a: relative importance of attributes (%).Notes: The error bars denote 95% CIs. Method: Study 5bWe recruited 106 (89% female; Mage = 37.3 years) real consumers of Pivotte from the company's email listserv for a paid online survey. To evaluate their preferences, we employed a CBC survey with four attributes (i.e., price, style, color, and sustainability) with three levels within each attribute. Note that in this study, durability is not an attribute by itself but is framed as one of the levels within the sustainability attribute. Consistent with our conceptualization of the three dimensions of sustainability, as well as the company's existing strategy, the sustainability attribute, labeled as ""textile"" in the survey, consisted of three levels: the eco-friendly sourcing of materials (""Made with eco-friendly fabric with advanced waste-reduction technologies""), manufacturing process with fair labor practices (""Made in N.Y.C. by top manufacturers with impeccable labor practices""), and the durability of the clothing (""Made with durable, 4-way stretch, stain-resistant fabric that will last for years""; for a screenshot of what respondents saw, including all attributes and levels, see Web Appendix W13). Results: Study 5bSimilar to Study 5a, we used Sawtooth's HB-Reg Module to estimate the models. Confirming the relevance of durability, we found that the part-worth utility of the durability message was highest (Mutility =.23), followed by sourcing of materials (Mutility =.10) and manufacturing process (Mutility = −.33).[16] The respondents preferred the durability level of sustainability to the manufacturing level, with 99.30% confidence, and to the sourcing level, with 72.82% confidence. Thus, there was a significant difference between the part-worth utilities of durability and manufacturing levels, but not between durability and sourcing levels.We also examined the relative importance weights across all attributes; the weights indicated that style was the most important attribute (44.63%; CI95% = [40.37, 48.88]), followed by sustainability (20.43%; CI95% = [16.92, 23.94]), color (17.98%; CI95% = [15.58, 20.39]), and price (16.96%; CI95% = [14.68, 19.23]). These results indicate that, in the case of Pivotte pants, style was significantly more important than the other three attributes. Notably, information about the sustainability of the product was as important as the product's price and color, suggesting that when durability was framed as a level of sustainability, sustainability emerged as an important and valued attribute for consumers. ReplicationIn Study 5b, we purposely labeled the sustainability attribute as ""textile"" to diminish potential demand effects. To increase the label's face validity, we also replicated Study 5b explicitly naming the attribute as ""sustainability"" on Prolific Academic (n = 150; 100% female; Mage = 36.4 years; Mincome ≥ $100,000). These results enable us to confirm that durability is an important dimension of sustainability independent of the specific label (see Web Appendix W12). DiscussionStudy 5a shows that when consumers have to trade off between durability and other product attributes, durability emerges as an important attribute that is second only to style and just as valued as price. Study 5b demonstrates that durability can be effectively positioned as a dimension of sustainability. In particular, when durability was compared with the other two dimensions of sustainability (i.e., sourcing and manufacturing), it was strictly preferred to fair manufacturing processes and comparable to eco-friendly sourcing of raw materials.In conclusion, Studies 5a and 5b offer additional managerial insights regarding durability and how to promote it. Findings from Study 5a suggest that, whenever possible, marketers of high-end brands should provide concrete estimates of products' life spans (e.g., three vs. five years) and promote the durable nature of their goods. The results of Study 5b highlight that marketers can position durability as an appealing sustainability dimension that consumers genuinely value. General DiscussionThe present research finds that purchasing luxury can be a unique means to engage in sustainable consumption because high-end products are more durable. Yet consumers prefer to concentrate their budget on multiple ordinary goods over fewer high-end products. We demonstrate that this effect is, in part, driven by consumers' product durability neglect. Although consumers generally believe that more expensive products last longer, they fail to take such a notion into account when making purchases. Focusing on the domains of clothing and accessories, our studies explore durability as a central dimension of sustainability. Given that 10% of global carbon emissions arise from the fashion industry, nudging consumers toward fewer purchases of long-lasting, high-end apparel could lead to a reduction of emissions, thereby reducing a key factor driving global warming ([69]). Marketing ImplicationsOur findings show that high-end products can be more sustainable than mid-range products by virtue of their longer life cycle (Studies 1 and 2), and as Studies 4, 5a, and 5b indicate, durability can be strategically used to make high-end products more appealing. As such, the present research offers actionable strategies for marketers of high-end brands and products. Educating consumersOne potential challenge for marketers of high-end brands is to understand how to best educate their potential consumers in discerning the intrinsic high quality and durability of their goods. When we entered the term ""product durability,"" into the search engine AlsoAsked,[17] we found that two related queries included ""Why is durability important in a product?"" and ""How do you check durability?"" (see Web Appendix W14), suggesting that there is a demand to learn more about evaluating product durability. Marketers can take advantage of this opportunity to educate consumers through tutorials and advertisements or by making durability claims more concrete, as we did in Studies 4 and 5a. In fact, some luxury and premium brands have dedicated pages on their websites that specifically address this notion. For instance, Loro Piana underscores the exceptional durability of its Pecora Nera wool (https://ii.loropiana.com/en/our-world/pecora-nera) while Cuyana promises to deliver products that will ""last for years to come"" (https://www.cuyana.com/sustainability.html). Presumably, consumers who understand and can identify the characteristics that make products more durable should be more prone to choosing fewer high-end goods.In addition, government agencies and policy makers can take an active role in educating consumers about product durability. Public campaigns might encourage consumers to think of product durability and recognize long-lasting materials when making purchases. For example, the French national anticounterfeiting committee CNAC, in collaboration with many high-end brands (e.g., Van Cleef & Arpels, Chanel), has conducted a campaign to educate consumers about the downsides of purchasing counterfeit luxury products, such as the inferior quality of these goods leading to shorter-term use ([18]). Luxury brands and government agencies can collaborate to educate consumers about purchasing fewer, better goods that benefit the consumers and the environment. The sharing economyProduct durability may be a vital element in the emerging sharing economy for luxury products. Companies such as Rent the Runway, DressYouCan, and Verstolo are revolutionizing how millennials consume high-end clothing and accessories. Rental models allow for maximum use of physical products, giving multiple consumers access to the same products over a prolonged period, while mitigating potential concerns such as dissatisfaction or satiation with the purchase. Durability becomes even more important in these contexts as the products must be able to sustain multiple uses. Slowing the fashion cycleMarketers and brands also have an active role in determining how quickly goods are consumed, as the speed with which brands launch new products influences how quickly the existing goods become old-fashioned and discarded ([ 6]). Indeed, many new lines and collections are designed to have quick turnovers as certain trends and aesthetics are meant to evolve from season to season ([17]; [40]). Some fast-fashion brands, such as Zara and H&M, launch new items at two-week cycles. Recently, however, some high-end brands have started to challenge this notion and advocate for slower fashion cycles. Louis Vuitton, Off-White, Gucci, and Dries Van Noten are actively trying to slow down their fashion cycles by creating collections with ""less unnecessary products"" and a focus on fewer, longer-lasting pieces that ""can remove the idea that just because it's last season, it's devalued"" ([27]; [37]). High-end brands slowing down the pace of the new collections may send a positive signal to consumers that they should buy less frequently and value the long-lastingness of the products. The dark sides of luxuryPertinent to our focal product category of luxury are the questionable and unethical practices often associated with the sourcing and production processes. For instance, certain luxury brands are known to use materials that may impede on consumers' desire to protect animal rights (e.g., inhumane sourcing of animal skin) or are produced by exploiting workers during the production process and devastate the local community (e.g., blood diamonds, products created by sweatshop laborers; [55]; [56]). Recognizing these darker sides of luxury, we acknowledge that product durability alone may not lead to comprehensively sustainable business practices. Consumer welfareDurability is ultimately a consumer-centric attribute that directly affects consumers' pocketbooks, as consumers decide for how long to keep their belongings and whether to resell them when no longer wanted. Further, owning and reselling durable products can positively influence consumers' happiness and feelings of empowerment ([19]; [67]). Although there may be a risk of dissatisfaction shortly after a purchase or satiation over time, these issues can be uniquely addressed through return policies, resale markets (as Study 1 demonstrates), product warranty and guarantees, or innovative business models such as rental subscriptions. Theoretical ImplicationsBy establishing product durability as a critical dimension of both sustainability and luxury, we hope that this article is the first step toward a deeper understanding of durability in marketing research. Future research could address several theoretical aspects related to product durability. Different types of durabilityAs previously discussed, we conceptualize durability in terms of both functional and stylistic benefits ([46]). Indeed, some high-end brands prominently advertise the long-lastingness and sturdiness of their products, as seen in the ""Buy Less, Demand More"" campaign by Patagonia (see Web Appendix W1). At the same time, others focus more on promoting the stylistic durability of their offerings, such as Farfetch's ""forever wardrobe"" advertisement, which maintains that Farfetch's collection of products will not go out of trend and can be timeless, long-lasting staples (see Web Appendix W1). From a theoretical standpoint, is there a hierarchy between the functional and stylistic elements of durability, or do they contribute equally to the construct of product durability? Is one of the two benefits a sufficient condition for product durability, or are both necessary for an item to be perceived as truly durable? Durability and frequencyAs previously mentioned, some work suggests that consumers exhibit usage frequency neglect when choosing between different appliances, such as microwaves, ice cream makers, and monitors ([50]). In this case, the overlooked decision factor is the frequency of use (i.e., how often a consumer uses the product). When should we expect to see product durability neglect versus frequency neglect? Given that durability is directly related to both how physically sturdy a product is as well as how timeless its style is, one hypothesis is that product durability neglect may apply to categories in which both functional and stylistic benefits are particularly relevant, such as apparel consumption (our focus) and possibly more hedonic products in general. In contrast, it is plausible that frequency neglect may be more relevant in utilitarian product categories, such as kitchenware. Other industriesThe present research has focused on the domains of clothing and accessories. Although we predict that our findings and insights will likely generalize to different industries and product categories, it may be a worthwhile pursuit to document consumers' choices and product durability neglect in other domains. For example, it is plausible that for product categories that are often bought in installments (e.g., dishwashers, refrigerators) or for which data on depreciation and maintenance is readily accessible (e.g., cars, phones), consumers may be more apt to open mental accounts and compute the costs per usage of these transactions ([32]; [62]) than for products that are typically paid in full at the time of purchase. Consistent with our results, if consumers can readily anticipate long-time use of a potential purchase, they may be less prone to product durability neglect and thus opt for the high-end option. Another potentially interesting industry to analyze is furniture. For instance, would IKEA be the equivalent of the fast-fashion brand, H&M? In line with the present research, it is plausible that product durability neglect also drives preferences for frequent purchases of inexpensive furniture in lieu of long-term investments in high-end furniture that will last many years. Future Research DirectionsOur research can be further applied to explore additional aspects of sustainability and luxury brands. High-end and luxury brandsThe present research examines high-end, luxury goods and lower-end, ordinary goods in the context of apparel consumption. To broaden the scope of our inquiry, we have not distinguished between high-end, premium brands (e.g., Patagonia, Woolrich) and top luxury brands (e.g., Hermès, Louis Vuitton). However, these brands vary significantly on the luxury spectrum ([39]). Thus, future research might adopt a more nuanced approach and explore the meaning of durability at a more granular level for different types of high-end brands. For instance, when the top luxury watchmaker Patek Philippe promotes product durability, it may have to make a strong claim to justify the purchase (i.e., an intergenerational claim implying that the watch will last across three generations, spanning over a century; see Web Appendix W1). However, it is possible that other watchmakers that are positioned as premium brands may be able to make effective product durability appeals with shorter life span claims. Negative perceptions of luxury consumptionDespite certain benefits associated with luxury consumption, such as attribution of status, preferential treatment, and affiliation with desirable social groups and mates ([ 7]; [33]; [70]), recent work documents many social costs associated with the consumption of high-end, expensive products. For example, consumers who own luxury goods are considered less warm and authentic and more driven by impression-management motives than consumers who do not own them ([ 9]; [23]; [28]; [30]). These negative perceptions may also be driven by a failure to consider the durability of high-end products at the observers' end. In fact, our preliminary data (available upon request), which explore how others judge luxury shoppers, demonstrate that high-end consumers who spend the same amount of money as consumers opting for more ordinary goods across the same time horizon are perceived as more wasteful and materialistic, even though they ironically purchase fewer products. Given this finding, future work could further explore the negative nuances associated with perceptions of high-end buyers and uncover how such perceptions may be ameliorated. Functional alibiIf some avoid purchasing high-end products because of the aforementioned wasteful and materialistic perceptions associated with such goods, would highlighting product durability possibly help consumers justify these purchases to themselves and others? If so, they may be able to use product durability as a functional alibi for purchasing high-end items and increase their willingness to buy these goods ([43]). Conceptions of wasteHow consumers define and conceptualize the term ""waste"" is also a topic that may further enhance our understanding of sustainability. While some may define ""waste"" purely in financial terms of wasting money (i.e., buying one expensive sweater when cheaper ones are available), others define waste in physical terms of wasting material objects (i.e., buying many inexpensive sweaters). From a financial perspective, it may seem more wasteful to spend more on a single item. However, from a sustainability perspective, it may seem more wasteful to purchase an abundance of cheaper clothing that will deteriorate quickly and be thrown away. One hypothesis that warrants further investigation is whether having different conceptions of waste (i.e., overspending financially vs. overconsuming physically) lead to different consumption behaviors. For instance, some consumers may not consider spending money on high-end purchases negatively but, instead, penalize a ""quantity over quality"" mentality. Indeed, in a follow-up study (available upon request), we found that those who were more averse to wasting physical objects (vs. wasting money) judged high-end consumers (who own fewer items) less negatively than consumers of multiple mid-range items. ConclusionWe propose that luxury goods possess a unique, sustainable trait as they can have a longer life span than lower-end products. Despite the long-lasting nature of high-end goods, sustainable luxury can be a paradoxical concept for consumers, as many of them neglect the durability inherent in luxury products. With growing concerns about sustainable consumption, many luxury brands are increasingly becoming more committed in their efforts to embrace sustainability. Focusing on and promoting product durability could be an effective strategy to align a sustainability dimension with a high-end positioning while encouraging consumers to engage in a more sustainable consumption lifestyle for a better world. " 8,Carbon Footprinting and Pricing Under Climate Concerns," This article studies how organizations should design a product by choosing the carbon footprint and price in a market with climate concerns. The authors develop a model and first show how the cost and demand effects of reducing the product carbon footprint determine the profit-maximizing product design. They find that stronger climate concerns reduce the product carbon footprint, demand, the overall corporate carbon footprint and profit, but have an ambiguous impact on price. Next, the authors establish that offsetting carbon emissions can create a win-win outcome for the firm and the climate if the cost of compensation is sufficiently low. Going net zero leads to a win for society if the cost of offsetting is sufficiently low compared to the social cost of pollution created by the corporate carbon footprint. Third, the authors show how regulation in the form of a cap-and-trade scheme or a carbon tax affects product design, firm profitability, and green technology adoption. Finally, the authors extend the analysis to a competitive scenario and show that going net zero creates a win-win-win outcome for the firm, the climate, and society if the offset technology is sufficiently effective.","The consequences of climate change have become apparent and touch every corner of our society. Public opinion has reached a point where ""business as usual"" is hard to justify, and many organizations are pressed to find solutions. For instance, ""flight-shaming"" and the European Green Deal ([22]) pose a threat to the business model of airlines ([ 7]). Amid much fanfare, most major carriers are studying or already adopting approaches that are broadly in line with the three-step process ""measure, reduce, compensate"" outlined in the United Nations Climate Neutral Now initiative ([57]), essentially pledging to operate ""net-zero"" flights in the short run. Similarly, the automotive industry must urgently find ways to replace combustion engines to meet the increasing demand for low-emission vehicles and more stringent emission targets ([27]). Car makers around the world are rushing to bring electric vehicles to the market at reasonable prices.These recent developments, which generalize to organizations in logistics, fashion, retailing, and other sectors in the economy, underscore the importance of understanding climate concerns and making the necessary adjustments to one's offerings and prices. Marketing professionals play a critical role here because they are often tasked with sensing changes in consumer preferences and channeling them within an organization. According to a recent article, ""chief marketing officers should be involved in the development of the sustainability strategy based on what they can bring to the table: customer data, market analysis and audience insights"" ([ 8]). At the same time, however, marketing officials may lack the confidence to contribute to the debate and provide meaningful guidance to internal stakeholders ([43]).This article develops a model that helps marketers address climate concerns by optimizing carbon footprinting and pricing. It is well documented that consumers have climate concerns ([63]; [64]) and that media coverage of climate change motivates consumers to make more sustainable consumption decisions ([11]; [30]). The starting point of our analysis is a monopoly setting in which the firm designs a product by choosing its product carbon footprint and price, hereinafter referred to simply as product design. Calculating product carbon footprints—the climate impact per unit of product in carbon dioxide equivalent (CO2 eq) emissions—is now common practice ([42]; [60]), and these footprints are routinely certified based on international accounting standards ([26]; [34]). Using terminology from the [26], we define the product carbon footprint as ""cradle-to-gate emissions,"" which include production emissions (Scope 1) and emissions from purchased energy (Scope 2).[ 5] Importantly, reducing the product carbon footprint has both a cost effect due to the change in the unit cost to produce a greener product and a demand effect due to consumers' climate concerns.The key difference from a standard model where the firm chooses price and (environmental) quality is that the total number of purchases made by consumers determines not only the firm's profit but also its corporate carbon footprint ([28])—the aggregate climate impact of the firm across all units sold. The corporate carbon footprint causes a market externality that depends on the strength of the climate concerns and that a regulator may want to control. Figure 1 illustrates how the interplay between firm and consumers drives the market outcome (comprising product design, firm performance, and climate impact) and climate externality, and the role played by the regulator intervening to limit corporate carbon footprints.Graph: Figure 1. The interplay between firm, consumers, and regulator and the resulting market outcome under climate concerns.We derive three key results from this initial framework. First, we show how the profit-maximizing product carbon footprint depends on the relative size of the cost and demand effects of reducing the product carbon footprint. This insight reflects the familiar return-on-quality logic in the marketing literature ([50]; [51]; [52]) but accounts for consumers' climate concerns.Next, we show the impact of stronger climate concerns on the profit-maximizing product design. We demonstrate that it is optimal for a firm to decrease the product carbon footprint, and analyze the impact on price. In addition, we show that stronger climate concerns reduce firm profit.Finally, we show that stronger climate concerns reduce the overall corporate carbon footprint. This result occurs because stronger climate concerns reduce both the product carbon footprint and demand, which leads to a reduction in overall emissions. In other words, the greener product design further contributes to the reduction in overall emissions that results from the lower sales volume. Listening to the voice of consumers who demand greener products therefore helps organizations to reduce their corporate carbon footprint.We then extend the analysis in several directions. First, we consider the profitability of carbon offsetting, which compensates for a carbon footprint by reducing, avoiding, or sequestering carbon emissions elsewhere on the planet ([25]). Carbon offsetting is feasible because carbon emissions are a global (rather than local) environmental problem. Projects that result in carbon offsets tend to focus on renewable energy (such as building wind farms that replace coal-fired power plants) or carbon sequestration in soils or forests (such as agroforestry and tree-planting activities). Specifically, we allow the firm to purchase carbon offsets to attain a net-zero corporate carbon footprint. As a result, a firm may be able to offer a climate-neutral product even if its carbon footprint prior to offsetting is positive. We show that it is optimal for firms to go net zero if the compensation cost is sufficiently low relative to the demand-enhancing effect of reducing the product carbon footprint to net zero. In this case, going net zero is a win-win strategy for the firm and the climate.Second, we examine the profit-maximizing product design from a welfare perspective, effectively complementing the profit motive of the firm with respect for the environment and social justice ([ 6]; [32]; [35])—often referred to as the triple bottom line of profit, planet, and people ([21]). We show that, in the absence of carbon offsetting, the profit-maximizing corporate carbon footprint generally deviates from the socially optimal level. A net-zero corporate carbon footprint, in turn, is economically efficient if the cost of offsetting is sufficiently low compared with the social cost of the corporate carbon footprint.Third, we analyze how carbon regulation affects product design and the corporate carbon footprint. We study three common market interventions ([66]): carbon caps, cap-and-trade systems, and a carbon tax. We find that these interventions typically reduce firm profit. In addition, we show that these instruments are generally effective in curbing both the product carbon footprint and the corporate carbon footprint when taking the profit-maximizing price response into account. We also show how carbon regulation can accelerate green technology adoption.Finally, we extend our analysis to competition and illustrate how competitive carbon offsetting emerges in equilibrium if the offset technology is sufficiently effective. From a policy perspective, this suggests that providing efficient carbon removal technologies can accelerate the transition to a low-carbon economy. Table 1 provides an overview of the key findings and highlights the insights for marketers.GraphTable 1. Key Results and Insights for Marketers. Taken as a whole, our results contribute to research on green product development ([10]) by showing how carbon footprinting and pricing are determined by the interplay of consumers' climate concerns ([37]), firm technology, and market regulation ([49]). By endogenizing product design, this article also adds to the return-on-quality literature ([50]; [50]; [52]). Importantly, we provide a welfare analysis to understand the implications of product-design decisions for corporate social responsibility and thereby add to the sustainability literature in marketing ([ 9]; [14]; [32]; [39]; [47]). Finally, we extend [10] and related literature in supply chain management and engineering ([12]; [17]; [29]; [67]) by accounting for the climate externality and providing the first analysis of carbon offsetting.Our results also contribute to the literature on regulation in economics ([ 4]) by showing how carbon caps and carbon taxes ([13]) affect product design. In addition, we show that climate regulation can trigger investments in green technologies, thereby adding to the insights of [49] on the dynamic impact of regulation and the economics of climate science more broadly ([31]; [46]; [54]). The ModelConsider a firm that designs a product (or service) by choosing the price p≥0 and product carbon footprint κ∈[0,κ¯] . The set [0,κ¯] indicates the technologically feasible product carbon footprints, where the firm offers a green product with zero emissions if κ=0 and a maximally polluting brown product if κ=κ¯ . The technology of the firm results in the unit cost function c(κ) defined on [0,κ¯] , where c′(κ)≠0 is the change in unit cost in response to a change in the product carbon footprint κ.[ 6] If c′(κ)<0 , reducing the product carbon footprint increases unit cost. The opposite is true if c′(κ)>0 .We consider a market with consumers who have climate concerns and evaluate the product based on not only its intrinsic features and price p but also its carbon footprint κ. Without loss of generality, the mass of consumers is normalized to unity. A buyer derives utility u(κ,p;λ)=v−p−z(κ;λ)−E, Graph1where v∈[0,∞) is the valuation of the intrinsic features; z(κ;λ)≥0 measures the disutility from purchasing a product with carbon footprint κ, with λ≥0 capturing the strength of climate concerns; and E≥0 is the disutility from the climate externality caused by other buyers. Because a single buyer has no impact on the climate externality, E is the same irrespective of whether or not the consumer purchases the product. By normalizing the (intrinsic) utility of the outside option to zero, a consumer purchases the product if v exceeds the perceived price p+z(κ;λ) .The unobserved valuation v is distributed independently across consumers according to the cumulative distribution function F(v) . The disutility z(κ;λ) is assumed to increase at an increasing rate in the product carbon footprint κ, reflecting the increasing guilt or ""cold prickle"" ([ 3]) of consumers from purchasing a product that affects the climate. Formally, letting subscripts denote first and second partial derivatives, the convexity assumption z(κ;λ) can be restated as zκ(κ;λ)>0 and zκκ(κ;λ)≥0 . We set the disutility to zero if consumers do not have climate concerns or if the product is green, that is, z(κ;0)=z(0;λ)=0 .[ 7] The other boundary case occurs if consumers have strong climate concerns, in which case we assume that limλ→∞z(κ;λ)=κ . We further assume that stronger climate concerns increase the disutility from a given carbon footprint, that is, zλ(κ;λ)>0 . Finally, we assume that stronger climate concerns increase the marginal disutility of increasing κ, that is, zκλ(κ; λ) > 0.Consumers purchase if the utility from the product exceeds the utility from the outside option. Therefore, the demand for the product is derived as D(κ,p;λ)=1−F[p+z(κ;λ)]. Graph2Demand is decreasing in the product carbon footprint and price. Interpreting the product carbon footprint as an inverse measure of product quality, a lower κ means higher quality and therefore higher demand. Lowering the product carbon footprint implies demand neutrality when consumers do not care about the climate impact of the product (Dκ=0) and demand expansion when consumers have climate concerns (Dκ<0) . The novel aspect of our modeling approach is that ""product quality"" affects not only demand but also the corporate carbon footprint (i.e., the overall climate impact of the firm).The corporate carbon footprint results from multiplying the product carbon footprint by demand and is therefore given by Φ=κ D(κ,p;λ) . Note that if buyers do not fully account for their carbon emissions, they create a climate externality—""the biggest market failure the world has seen"" ([54], p. 1). The climate externality results from adding up the noninternalized carbon emissions across buyers: E(κ,p;λ)=[κ−z(κ;λ)]D(κ,p;λ). Graph3This climate externality is reduced to zero when consumers have strong climate concerns ( z(κ;λ)=κ ) and equals the corporate carbon footprint if consumers do not care about purchasing a product that affects the climate ( z(κ;0)=0 ). Therefore, the corporate carbon footprint has an impact on all consumers if buyers do not fully account for the product carbon footprint when making their purchase decision. Product DesignThis section first derives the profit-maximizing product carbon footprint and price of a product. We then study the impact of stronger climate concerns on these variables. Finally, we consider the impact of product design on the corporate carbon footprint. We assume throughout that the profit function is strictly concave in κ and p and thus has a unique constrained global maximum. Product Carbon Footprint and PriceThe firm chooses the product carbon footprint κ and the price p of the product to maximize profit. More formally, the firm solves maxκ,p π(κ,p;λ)=[p−c(κ)]D(κ,p;λ) Graph4 s.t. 0≤κ≤κ¯. GraphThe profit function shows that the product carbon footprint and price have a dual impact on markup and demand. Proposition 1 characterizes the profit-maximizing product design with product carbon footprint κ* and price p*(κ*) . To facilitate exposition, all proofs are relegated to the Appendix. Proposition 1: If reducing the product carbon footprint lowers unit cost, the firm should offer a green product with κ*=0 at price p*(0) , irrespective of the demand effect. If reducing the product carbon footprint increases unit cost but not demand, then it is optimal to offer a brown product with κ*=κ¯ at price p*(κ¯) . Finally, if the demand effect is sufficiently strong compared with the cost effect, then it is optimal to offer a product with κ*∈(0,κ¯) at price p*(κ*) .Proposition 1 mirrors the familiar return-on-quality logic in the marketing literature ([50]; [50]; [52]) and has two important implications. First, if lowering the product carbon footprint reduces unit cost, then it is optimal to increase efficiency and thereby increase ""process quality"" ([15]; [16]). Green cost cutting is more attractive when lowering the product carbon footprint not only reduces cost but also increases demand ([48]). This result helps explain why many sustainability efforts increase firm profit ([65]).Second, if lowering the product carbon footprint increases unit cost, there may be a trade-off between the cost effect and the demand effect. Absent a demand effect, reducing the product carbon footprint below that of the brown product only results in higher unit cost and is therefore suboptimal under profit maximization. However, when the increase in demand outweighs the impact of higher unit cost, firms should reduce the product carbon footprint relative to the brown product. In contrast to cost-cutting sustainability, cost-increasing sustainability reflects the idea that ""major pressure for changing marketing practices may come from consumers themselves"" ([37], p. 133) and can be viewed as one of the ""sustainability programs worthy of the name"" ([19]). Figure 2 summarizes the product design strategies derived in Proposition 1.Graph: Figure 2. Profit-maximizing product design as a function of the cost effect (due to the change in unit cost) and the demand effect (due to consumers' climate concerns). Impact of Climate Concerns on Product DesignStronger climate concerns affect demand and thereby product design and firm profitability. The next result summarizes the implications. Proposition 2: If reducing the product carbon footprint lowers unit cost, stronger climate concerns do not affect the profit-maximizing product carbon footprint and price, and leave profit unchanged. Instead, if reducing the product carbon footprint increases unit cost, stronger climate concerns decrease the product carbon footprint, have an ambiguous impact on price, and reduce profit.Proposition 2 has two important implications. First, it shows how climate concerns affect the profit-maximizing product design. Stronger climate concerns increase the consumers' marginal disutility of raising κ, which motivates the firm to reduce the product carbon footprint to make the product more attractive to consumers. The impact on the price is ambiguous because stronger climate concerns not only increase the unit cost due to the lower product carbon footprint, but also compress the price-cost margin.[ 8] Note that if we interpret the product carbon footprint as an inverse measure of product quality, then Proposition 2 implies an ambiguous relationship between product quality and price, which contributes to the literature on price-quality relationships ([24]; [48]).Second, Proposition 2 implies that a monopoly firm has a motive to downplay climate concerns due to their negative impact on profit. This suggests an intuitive explanation for ""dither and denial"" ([62]) by polluting firms in the face of climate change ([38]; [40]). This result also points to a potential tension between product managers who tend to focus on profit and managers who are in charge of corporate social responsibility. As we will show next, one way firms can resolve this tension is by broadening the scope of performance measurement beyond profit to include climate and societal impact. Corporate Carbon FootprintThe first two propositions extend the logic of profit-maximizing product design to a setting where consumers have climate concerns. The goal of this subsection is to provide new insights on how changes in climate concerns affect the climate impact of the firm. Proposition 3: Stronger climate concerns reduce demand and the corporate carbon footprint Φ*=κ* D(κ*,p*;λ) .Proposition 3 shows that stronger climate concerns necessarily reduce the corporate carbon footprint. The reason is that stronger climate concerns reduce both the product carbon footprint and demand, which leads to a reduction in overall emissions. This is a strong result, because the positive demand effect of offering a greener product could be expected to compensate for the lower product carbon footprint—similar to the rebound effect from technological progress (Alcott 2005), which suggests that higher efficiency leads to an initial reduction in demand for a resource that is outweighed by a corresponding increase in demand due to relatively lower resource cost (""Jevons paradox""). In our setting, the rebound effect cannot occur because stronger climate concerns increase the disutility from a given carbon footprint (zλ (κ; λ) > 0), which translates into an overall reduction in demand. In contrast, in a setting where zλ (κ; λ) < 0, stronger climate concerns may lead to a rebound effect because of the resulting increase in demand, an outcome that is conceivable if consumers use the brown product (rather than the green product) as a reference point.[ 9] In sum, whenever listening to the voice of consumers leads to greener product design and lower demand, the consumer pressure for greener products motivates profit-maximizing organizations to become greener, even though the impact on profit is negative. Proposition 3 thus suggests that consumers play an important role in making both products and organizations greener. Carbon OffsettingWhile producing a green product is perhaps the most obvious means for a firm to achieve climate neutrality, an increasingly popular alternative is to adopt an offset strategy whereby the corporate carbon footprint is fully compensated for (by funding projects that achieve an equivalent level of carbon dioxide saving), thereby creating a net-zero corporate carbon footprint. While carbon offsetting is arguably not the solution to climate change, it allows firms to achieve climate neutrality even if the available production technology does not yet allow it. In principle, any company can go net zero by buying offset services (that promote the planting of trees, renewable energy, etc.) from providers such as Carbon Footprint Ltd or Gold Standard.Accordingly, the purpose of this section is to study under what conditions firms can benefit from adopting an offset strategy. Suppose that an offset provider charges a fixed price ω≥0 per unit of carbon offset. The firm then chooses the product carbon footprint κ and the price p to maxκ,p π(κ,p;ω)=[p−c(κ)−ωκ]D(0,p) Graph5 s.t. 0≤κ≤κ¯, Graphwhere ωκD(0,p) is the total offsetting cost of reaching a net-zero corporate carbon footprint if the product carbon footprint prior to offsetting is κ. That is, with carbon offsetting purchase decisions and demand depend on the net-zero product carbon footprint rather than the product carbon footprint prior to offsetting. The next result points to the possibility of a win-win outcome for the firm and the climate, where the benchmark is provided by the no-offset strategy. Proposition 4: Adopting an offset strategy is optimal for a firm if the compensation cost is sufficiently low compared with the additional profit from the demand-enhancing effect of reducing the product carbon footprint to net zero. Stronger climate concerns make the adoption of an offset strategy more attractive. The downside of an offset strategy is that it motivates a firm to increase the product carbon footprint before offsetting if the price per unit of carbon offset is sufficiently low.Proposition 4 shows that offsetting carbon emissions can boost profit and fight climate change. The key driver of this result is that relieving consumers from disutility resulting from consuming a product with a positive carbon footprint has a demand-enhancing effect that directly translates into higher profit. A firm is more likely to adopt an offset strategy if the price per unit of carbon offset is low. This suggests that providing low-cost carbon offset options to firms might curb their corporate carbon footprints even when the standard tools of carbon regulation have no bite. Corporate Social ResponsibilitySustainability is an umbrella term generally viewed as comprising economic profitability, respect for the environment, and social justice ([ 6]; [32]; [35]). To integrate these three ingredients into the analysis, we say that a firm behaves in a manner that is consistent with corporate social responsibility if it maximizes welfare. To do so, it must consider the triple bottom line of profit (firm and offset provider), planet (climate impact), and people (consumer surplus). Our next result shows that the adoption of an offset strategy can create a win-win-win outcome. Proposition 5: Without offsetting, the corporate carbon footprint is generally nonzero and different from the socially optimal level. Adopting an offset strategy that leads to net-zero carbon emissions improves welfare if the cost of carbon offsetting is sufficiently low compared with the social cost created by the corporate carbon footprint.Proposition 5 confirms the notion that focusing exclusively on profit leads firms to make decisions that are generally inconsistent with corporate social responsibility. Intuitively, a firm has an incentive to strategically distort the product carbon footprint to exploit pricing power, which leads to an economically inefficient product carbon footprint ([53]). Interestingly, under an offset strategy, profit-maximization may result in a net-zero corporate carbon footprint even if it is socially undesirable to fully compensate for the emissions because the firm does not factor in the social cost of carbon removal. However, if the carbon removal technology is sufficiently cost effective, the win-win outcome for the firm and the climate under an offset strategy translates into a win-win-win outcome and therefore produces benefits for society at large.In addition, Proposition 5 sheds light on the controversial debate about carbon offsets that ""have been used by polluters as a free pass for inaction"" ([58]). The cost efficiency of carbon offsetting stems from the fact that emissions are compensated for in places where the cost of offsetting is low, typically in developing countries. While this makes sense from an economic perspective, managers have to bear in mind ""whose mess this is"" and that ""some of these places would welcome investment in reforestation and afforestation, but they would also need to be able to integrate such endeavours into development plans which reflect their people's needs"" ([20]). Carbon RegulationRegulators increasingly try to limit carbon emissions of firms to meet climate targets and address climate change. The most recent examples include the Green New Deal in the United States and the European Green Deal, which address climate change by introducing various regulatory interventions. We show how a firm should respond to carbon caps, cap-and-trade systems, and carbon taxes, which are by far the most common regulatory market interventions today ([66]), and study their impact on expected firm profitability. While the institutional details of these interventions vary across industries and legislations, we focus on their key characteristics and show that the risk of regulation accelerates investments in green technology. Carbon CapsThe most direct approach to limit the corporate carbon footprint is to impose a binding carbon cap R≥0 . An example is the European Union's fleet-wide binding emissions target for new cars imposed on manufacturers ([23]). In the face of such regulation, the firm solves the following problem: maxκ,p π(κ,p;λ)=[p−c(κ)]D(κ,p;λ) Graph6 s.t. 0≤κ≤κ¯ and Φ(κ,p;λ)≤R, Graphwhere Φ(κ,p;λ) is the corporate carbon footprint. The next result summarizes the impact of a binding carbon cap. Proposition 6: A binding carbon cap reduces the corporate carbon footprint and profit, and translates into a lower product carbon footprint if the sales expansion that results from offering a greener product is sufficiently small.A binding carbon cap has the obvious effect of reducing the corporate carbon footprint and profit. More interestingly, restricting overall emissions forces the firm to adjust the profit-maximizing product design by lowering the product carbon footprint because this helps to relax the carbon constraint if the sales expansion from offering a greener product is sufficiently small compared with the direct reduction in overall emissions. In real-world markets, however, carbon caps are often coupled with a carbon market, where firms can sell or purchase carbon allowances, which gives rise to cap-and-trade systems. Cap-and-Trade SystemsThe leading examples of cap-and-trade systems are California's Cap-and-Trade Program, the Chinese National Carbon Trading Scheme, and the European Union Emissions Trading System. Cap-and-trade systems have an important advantage over carbon caps: firms with low compliance costs can sell carbon allowances in the emissions market and turn them into a source of revenue. For example, Tesla generates significant revenues by selling zero-emission vehicle credits in the United States ([41]).The society's need to tackle climate change creates considerable uncertainty for businesses regarding their regulatory environment. To address how a firm can proactively deal with the possible introduction of regulation, we assume that a regulator is expected to implement a cap-and-trade system with probability ρ∈[0,1] , with a given carbon cap R≥0 . Under regulation, the firm can choose between two options: ( 1) adjust the product design to meet the potential regulatory constraint at the firm level (profit πr ) or ( 2) stick to the current product design and purchase carbon allowances at a market price ϖ≥0 . The following result summarizes the impact of a binding carbon cap coupled with the possibility to buy carbon allowances in the emissions market. Proposition 7: The expected cost of a cap-and-trade system to the firm is given by ρmin{π*−πr,ϖ(Φ*−R)} , where ρ(π*−πr) is the expected reduction in profit if the firm complies with the carbon cap by adjusting product design, and ρϖ(Φ*−R) is the expected reduction in profit if the firm purchases carbon allowances to offset the emissions. The expected cost increases when the implementation probability ρ is higher, when the carbon cap R is more severe, and when the carbon price ϖ is higher.Proposition 7 confirms the intuition that uncertain cap-and-trade regulation reduces the expected profit of the firm. Furthermore, the cost of regulation to the firm is increasing in the probability of regulation and the market price for emissions. This is important because companies should anticipate changes in the regulatory environment and thus want to invest in the adoption of a greener technology to comply with expected regulation. Carbon TaxIn December 2019, the International Monetary Fund issued a report suggesting that a global average carbon price of $70 a ton would be sufficient for many (but not all) countries to meet their Paris accord mitigation targets ([33]). While a carbon cap directly limits the climate impact of the firm, such a price alters the cost structure of the firm with the goal of reducing the corporate carbon footprint to a socially desirable level. To reflect this, assume that t≥0 is the fixed (Pigouvian-style) tax rate on carbon emissions. Under such a proportional carbon tax, the firm solves maxκ,p π(κ,p;λ,t)=[p−c(κ)−tκ]D(κ,p;λ) Graph7 s.t. 0≤κ≤κ¯. GraphThe next result summarizes the impact on the product design and firm profit, where the optimized profit under a carbon tax is denoted by π*(t) . Proposition 8: A proportional carbon tax reduces not only the profit-maximizing product carbon footprint but also the corporate carbon footprint. The expected cost of taxation is given by ρ{π*(0)−π*(t)}>0 , which increases in the probability ρ that a tax will be implemented and in the tax rate t.Proposition 8 shows that a higher carbon tax effectively reduces the corporate carbon footprint via its impact on the profit-maximizing product carbon footprint and price in response to higher cost, which leads to an overall reduction in sales. The result also shows that the uncertain introduction of a carbon tax reduces expected profit: The increase in the carbon tax increases unit cost, but only part of this can be passed on to consumers in the form of higher prices—a result akin to the imperfect pass-through of trade deals documented in the channels literature ([44]; [45]). The adverse impact on profit helps explain why firms lobby against regulation ([61]).That said, the result also shows why an offsetting strategy is an interesting option: a net-zero corporate carbon footprint makes regulation unnecessary and has an immediate positive impact on the climate. A carbon tax, in turn, affects the product carbon footprint and raises revenue for the government without offsetting the emissions. However, carbon offsets do not provide an incentive for firms to invest in green technologies and are therefore often considered an interim measure until new technologies become available. Green Technology AdoptionThe need to comply with carbon regulation may trigger investments in green technologies. To demonstrate this, we consider the case of a carbon cap and assume that an existing brown technology c0(κ) can be replaced with a green technology c1(κ) at a fixed cost f1>0 . This green technology enables the firm to reach any product carbon footprint at a lower unit cost, that is, c0(κ)≥c1(κ) for all κ. Letting ρ denote the probability that carbon regulation becomes effective, we derive the following result. Proposition 9: The threat of carbon regulation stimulates green technology adoption if the anticipated carbon regulation reinforces the profit advantage of adopting the green technology.Proposition 9 shows that regulatory risk provides an incentive for the firm to adopt the green technology. In other words, the mere threat of carbon regulation can prompt a reduction of the corporate carbon footprint and lead to process innovation ([49]). More broadly, from a policy perspective, the threat of effective regulation allows the government to put some of the burden of technology adoption on the shoulders of the firm. Competitive StrategyIn this section, we extend the baseline case to include competition. We first describe the interaction between two firms and consumers and then study conditions under which adopting an offset strategy is consistent with pursuing a triple bottom line. SetupWe consider a market with two single-product firms i=1,2 that simultaneously choose the product carbon footprint κi and price pi . The technology of firm i is represented by the unit cost function ci(κi)=ci0(1−κi)2 , where ci0>0 is a firm-specific cost parameter. Carbon offsetting to achieve a net-zero product carbon footprint is provided by an independent provider at cost ω≥0 per unit of carbon emissions. The carbon removal technology of the provider is represented by the unit cost ϕ≥0 and fixed cost F>0 . Each firm can choose between two strategies: a no-offset strategy where the product is marketed with carbon footprint κi , or an offset strategy where the product is marketed with a net-zero carbon footprint.The products are differentiated horizontally and vertically. Horizontal differentiation is à la Hotelling and reflects consumer heterogeneity with respect to intrinsic product features. We assume that the firms are located at the extremes of the characteristics space [0,1] , that is, x1=0 and x2=1 . Vertical differentiation on the carbon footprint reflects the notion that a lower product carbon footprint enhances the worth of the product in the minds of consumers. Category demand is fixed, and the market consists of a unit mass of consumers. We assume that individual preferences are described by the conditional indirect utility function ui(κi,pi;λ)=v−pi−zi(κi;λ)−12|x−xi|−E, Graph8where v is the valuation of the intrinsic product features, zi(κi;λ)=λκi is the disutility from purchasing a product with carbon footprint κi , and E≥0 is the disutility from the climate externality caused by other buyers in the market. Following convention, we let x∈[0,1] denote the consumer's preferred product characteristic and |x−xi| denote the horizontal distance to the product of firm i ([ 2]). The preferred product characteristics are drawn independently across consumers from a uniform distribution over the interval [0,1] . Demand for the product of firm i as a function of the product carbon footprints κ=(κ1,κ2) and prices p=(p1,p2) can be derived as Di(κ,p;λ)=12−λ(κi−κj)−(pi−pj). Graph9Each firm can therefore obtain a competitive advantage over its rival by offering a product with a lower carbon footprint, by charging a lower price, or both. Competitive Carbon OffsettingIn a setting with two firms and two strategic options per firm, there are a total of four possible outcomes: both firms adopt a no-offset strategy, both firms adopt an offset strategy, or one firm adopts an offset strategy while the other firm adopts a no-offset strategy. To illustrate the emergence of competitive carbon offsetting, we consider symmetric firms with c10=c20=1 and let λ = 1 represent strong climate concerns. The following result holds. Proposition 10: Suppose that firms are symmetric and consumers have strong climate concerns. Then, if the offset technology is sufficiently effective, each firm benefits from adopting an offset strategy irrespective of the rival's choice of strategy. This leads to a net-zero industry carbon footprint and improves welfare.To understand the intuition for this result, consider the profits that each firm can earn in the four possible outcomes represented in Figure 3. From firm 1's point of view, it is always more profitable to adopt an offset strategy than a no-offset strategy, no matter whether firm 2 chooses a no-offset strategy (which yields profit A>14 ) or an offset strategy (which yields profit 14>B ). Because the firms are symmetric, the same logic applies to firm 2, no matter whether firm 1 chooses a no-offset strategy or an offset strategy. In other words, each firm can create a win-win for itself and the climate by adopting the offset strategy—irrespective of the rival's choice of strategy. Therefore, the offset strategy is strictly dominant for each firm, and competitive carbon offsetting emerges in equilibrium.Graph: Figure 3. Possible outcomes and corresponding profits for c10=c20=1 and λ=1, where the top left number is the profit of Firm 1 and the bottom right number the profit of Firm 2.Interestingly, Proposition 10 further shows that if the offset technology is sufficiently cost effective, competitive forces can create a win-win-win outcome for each firm, the climate, and society. Therefore, choosing an offset strategy is consistent with pursuing corporate social responsibility. This has an important implication for policy makers: Providing efficient carbon removal technologies can accelerate the transition to a zero-carbon economy by providing incentives for firms to offer products and services with a net-zero product carbon footprint. DiscussionThis article explored how organizations should design a product by choosing the carbon footprint and price in a market with climate concerns. We also analyzed how changes in product design affect profitability and the organization's overall climate impact—the corporate carbon footprint. Furthermore, we analyzed how offset strategies and carbon regulation can be used to limit the corporate carbon footprint, and how they affect green technology adoption. Finally, we examined the role of competition for product-design decisions and carbon offsetting.Throughout, the underlying objective was to help marketing professionals understand how climate concerns translate into optimal carbon footprinting and pricing decisions. With this in mind, the current section elaborates on the implications of our results for organizations, policy makers, and consumers. We end by discussing some of the limitations of our work and avenues for future research. Implications for OrganizationsWhen confronted with climate concerns, the first response of an organization should be to assess the carbon footprint of its product and understand the impact on cost. If it is possible to reduce the product carbon footprint and reduce cost, eliminating waste (e.g., improving energy management) and adjusting price is the obvious consequence. However, if reducing the product carbon footprint is costly, it is imperative for marketers to understand the trade-off with demand (via, e.g., market research). They can then advise their organizations on how to adjust the product design in response to stronger climate concerns.Another key consideration is how changes in product design affect the organization's overall climate impact. Because marketers lower the product carbon footprint and adjust price in response to stronger climate concerns, the changes in product design lead to a lower corporate carbon footprint as both the product carbon footprint and demand are reduced. Therefore, greener products lead to greener organizations. At the same time, the consumer pressure for greener products reduces profit. To save the cost of making the product greener, organizations may decide to go net zero by offsetting the carbon emissions. For example, UPS offers a carbon-neutral shipping service with net-zero carbon emissions ([59]), while Kering ""will become carbon neutral within its own operations and across the entire supply chain"" ([36]). EasyJet announced its decision to go net zero and claims to be ""the first major airline to offset the carbon emissions from the fuel used for every single flight"" ([18]). In theory, net-zero carbon footprints are consistent with corporate social responsibility if the social cost of carbon compensation is sufficiently low. However, in practice, carbon compensation remains an imperfect solution that falls short of green product development.A third consideration is whether competition forces otherwise brown organizations to offset their carbon emissions. We showed that competition has the potential to prevent an industry from a race to the bottom where firms offer brown products. For instance, several European airlines offset their carbon emissions on domestic flights to compete for climate-concerned consumers. Implications for Policy MakersWhile carbon regulation effectively limits the organization's overall climate impact, it imposes a cost of regulation on organizations. This is arguably the reason why policy makers hesitate to implement effective carbon regulation. Our analysis shows that the mere threat of regulation negatively affects firm profitability. On the positive side, a well-designed market intervention benefits consumers and society at large, and stimulates green technology adoption.More generally, our work suggests that society should put a price on carbon emissions. Carbon offsets and carbon taxes achieve this goal. However, carbon offsets do not provide an incentive for firms to invest in greener technologies and, therefore, should be considered an interim measure until new technologies become available. The recent call by the United Nations Global Compact to set an internal price at a minimum of $100 per metric ton by 2020 is an attempt to price carbon emissions and put climate change at the heart of corporate strategy ([56]). Implications for ConsumersOur research suggests that organizations should act on consumers' climate concerns even if it reduces their profit, because not doing so would result in even lower profit. Voicing stronger climate concerns in our model necessarily reduces the corporate carbon footprint. However, in pressuring organizations into offering greener products, consumers may end up paying higher prices.In addition, our research shows that consumers with stronger climate concerns cause a smaller climate externality and thereby reduce the burden they impose on society. Stronger climate concerns also increase the profitability of carbon offsetting, which may stimulate the transition to a net-zero carbon economy. More broadly, our analysis suggests that ""green consumerism"" has a real impact on market outcomes. Limitations and Future ResearchFuture research could study how climate concerns are shaped and how they affect the consumers' utility function. Exploring preferences is key to understanding the impact of stronger climate concerns on product design and the overall corporate carbon footprint. One approach is to assume that climate concerns are influenced by opinion leaders. Another option is to assume that the organization can influence climate concerns via persuasive advertising. Yet another issue for future research is whether changes in climate concerns monotonically affect purchase decisions.Second, future research could consider emissions that occur during the consumption stage (Scope 3). This would allow marketers to understand what drives the life-cycle carbon footprint of a product (a cradle-to-grave approach). The interesting aspect of such an extension is that the emissions in the consumption phase are driven by consumer behavior that cannot be easily influenced by the firm.Third, it would be interesting to study the role of competition in a more nuanced way. A limitation of our approach is that it ignores the possibility of market expansion. Researchers could also study the impact of carbon regulation and taxation on industry dynamics and their potential to accelerate the transition to a zero carbon economy.Overall, this article highlights some of the complexities and consequences of climate concerns on product design and corporate carbon emissions. Hopefully, this will spur further research into understanding the impact of climate-dependent preferences and exploring the system-wide effects of government actions, including the determination of offset prices. We also hope to see multiple approaches brought to bear in the area, including agent-based simulations, data-based empirical analyses, and natural experiments. Ideally, this will lead to creative regulations and behaviors that result in win-win-win outcomes for consumers, organizations, and the environment or at least, absent that, better understanding of the trade-offs being made among the three parties. AppendixProof of Proposition 1. Assuming that the profit function π(κ,p;λ) is strictly concave in (κ,p) , the profit-maximizing product carbon footprint κ* and price p* must satisfy the following necessary and sufficient Kuhn–Tucker conditions (the multipliers μ1≥0 and μ2≥0 are associated with the inequality constraints): −c′(κ*)D(κ*,p*;λ)+[p*−c(κ*)]Dκ(κ*,p*;λ)+μ1−μ2=0, GraphA1 D(κ*,p*;λ)+[p*−c(κ*)]Dp(κ*,p*;λ)=0, GraphA2 μ1κ*=0 and μ2(κ*−κ¯)=0. GraphDepending on the slope of the unit cost function, we distinguish two cases. First, we consider the case where c′(κ)>0 . Suppose that Dκ=0 and κ*>0 . Then, Equation A1 leads to a contradiction as μ1=0 , so that κ*=0 . This result holds a fortiori if Dκ<0 . Second, we assume that c′(κ)<0 . If Dκ=0 , then a solution that involves κ*<κ¯ leads to a contradiction in Equation A1, so that κ*=κ¯ . Next, if Dκ<0 , then the choice of the product carbon footprint is governed by the relative strength of the cost effect and the demand effect of increasing κ: If −c′(0)D+[p*−c(0)]Dκ≤0 , then κ*=0 , whereas if −c′(κ¯)D+[p*−c(κ¯)]Dκ≥0 , then κ*=κ¯ ; otherwise, there is an interior solution with κ*∈(0,κ¯) . □Proof of Proposition 2. First, from Proposition 1, κ*=0 and p*(0) if c′(κ)>0 . Because D(0,p*[0];0)=D(0,p*[0];λ) for λ>0 , stronger climate concerns leave product design and profit unchanged.Second, if c′(κ)<0 , there are two subcases: the emergence and the reinforcement of climate concerns. In the absence of climate concerns ( λ=0 ), profit at κ¯ is given by π(κ¯,p;0)=[p−c(κ¯)]D(κ¯,p;0) . Instead, when consumers have climate concerns ( λ>0) , profit at κ*≤κ¯ is given by π(κ∗,p;λ)=[p−c(κ∗)]D(κ∗,p;λ) . Because the emergence of climate concerns reduces demand and (weakly) increases unit cost, this implies that π(κ∗,p∗;λ)<π(κ¯,p0;0), GraphA3where p∗=argmaxpπ(κ∗,p;λ) and p0=argmaxpπ(κ¯,p;0) , which means that the emergence of climate concerns reduces profit. Instead, when climate concerns are reinforced, applying the envelope theorem yields π(κ∗,p∗;λ)Dλ=[p∗−c(κ∗)]Dλ(κ∗,p∗;λ)<0, GraphA4where Dλ=−F(p+z(κ;λ))zλ(κ;λ) from Equation 2 and zλ(κ;λ)>0 by assumption, which means that the reinforcement of climate concerns reduces profit.To understand the impact of reinforced climate concerns on product design, suppose that κ*∈(0,κ¯) , so that the multipliers μ1 and μ2 are zero in Equations A1 and A2. Substituting Equation A2 into Equation A1 yields −cʹ(κ*)−Dκ/Dp=0 , which can be expressed in model primitives as −cʹ(κ*)=zκ(κ*;λ) GraphA5by using that Dκ=−F(p+z(κ;λ))zκ(κ;λ) and Dp=−F(p+z(κ;λ)) . Applying the implicit function theorem to Equation A5 yields dκ∗dλ=−zκλ(κ∗;λ)cʺ(κ∗)+zκκ(κ∗;λ)<0, GraphA6because the denominator is positive by the concavity assumption and zκλ(κ*;λ)>0 by assumption. To see the impact of stronger climate concerns on price, note that Equation A2 can be expressed in model primitives as p*=c(κ*)+1−F(p*+z(κ*;λ))F(p*+z(κ*;λ)). GraphA7From the implicit function theorem, dp∗dλ=−[f(⋅)+(p∗−c(κ∗))fʹ(⋅)]zλ(κ∗;λ)2f(⋅)+(p∗−c(κ∗))fʹ(⋅)+cʹ(κ∗)dκ∗dλ. GraphA8The concavity assumption and zλ(κ*;λ)>0 imply that the first term on the right-hand side of Equation A8 is negative, whereas the second term is positive by Equation A6 and the assumption that cʹ(κ)<0 . Therefore, the impact of stronger climate concerns on the profit-maximizing price is ambiguous.Proof of Proposition 3. The corporate carbon footprint results from multiplying the product carbon footprint by demand and can therefore be written as Φ*(λ)=κ*(λ)[1−F(p*+z(κ*;λ))]. GraphA9Differentiating Equation A9 with respect to λ yields dΦ*(λ)dλ=dκ*dλ[1−F(⋅)]−κ*f(⋅)[dp*dλ+zκdκ*dλ+zλ]  =dκ*dλ[1−F(⋅)]−κ*[f(⋅)]2zλ(κ*;λ)2f(⋅)+(p*−c(κ*))fʹ(⋅),  GraphA10where the second equality follows from substituting the expressions for dκ* /dλ and dp* /dλ in Equations A6 and A8, respectively, and using that −cʹ(κ*)=zκ(κ*;λ) from Equation A5. Because zλ(κ*;λ)>0 and 2F(⋅)+(p*−c(κ*))fʹ(⋅)>0 by the concavity assumption, stronger climate concerns reduce demand and therefore the corporate carbon footprint because Dκ*Dλ<0 by Proposition 2.Proof of Proposition 4. Suppose that c′(κ)<0 . Under carbon offsetting, the product has a net-zero carbon footprint, which implies that z(0,λ) = 0 and thus that demand D(0,p) is independent of λ. An offset strategy yields the profit π(κo,po;ω)=[po−c(κo)−ωκo]D(0,po), Graphwhere κo is the product carbon footprint prior to offsetting and po is the corresponding price. Noting that π(κ¯,po;ω=0)=π(κ¯,p0;λ=0) , it follows from Equation A3 that π(κ¯,po;ω=0)>π(κ*,p*;λ) . Applying the envelope theorem to the optimal profit under offsetting yields dπ(κo,po;ω)/dω=−κoD(0,po)<0 . This implies there exists ω¯ such that π(κo,po;ω)>π(κ*,p*;λ) for ω∈(0,ω¯) , which means that the firm can benefit from adopting a climate neutral strategy when ω is sufficiently low. Next, stronger climate concerns reduce profit in the benchmark case absent carbon offsets by Equation A4 ( dπ(κ*,p*;λ)/dλ<0 ), whereas they leave profit unaffected under an offset strategy ( dπ(κo,po;ω)/dλ=0 ) because demand is independent of λ. Consequently, stronger climate concerns make the adoption of an offset strategy more attractive to the firm.Absent carbon offsets, the optimal product carbon footprint is determined by the condition −cʹ(κ*)=zκ(κ*;λ) from Equation A5. Under an offset strategy, the firm chooses κo to maximize the markup p−c(κ)−ωκ because demand does not depend on the choice of κ. Therefore, at an interior solution, κo is determined by the condition −cʹ(κo)=ω . Clearly, κo>κ* if ω0 and conversely, which implies that the firm's choice of the product carbon footprint is not necessarily consistent with corporate social responsibility.Adopting an offset strategy is consistent with corporate social responsibility if it increases welfare compared with the no-offset strategy. To this end, consider an offset market in which an offset provider compensates emissions at variable cost ϕωκoD(po) , where ϕ∈[0,1] is an efficiency parameter, and fixed cost F>0 . In this scenario, welfare is obtained by adding up consumer surplus and the profits from the firm and the offset provider: W(κo,po;ω)=∫po∞vdF(v)−c(κo)D(0,po)−ϕωκoD(0,po)−F. GraphA13Because the offset cost ωκoD(0,po) is a transfer from the firm to the offset provider, it cancels out in the welfare calculation. Using Equations A12 and A13, adopting a climate neutral strategy is economically efficient if W(κo,po;ω)≥W(κ*,p*;λ) . This condition is satisfied if the social cost of carbon offsetting ϕωκoD(0,po)+F is sufficiently low compared with the climate damage that results from the corporate carbon footprint under a no-offset strategy, given by Φ(κ*,p*;λ) .Proof of Proposition 6. The profit-maximizing product carbon footprint and price satisfy the following Kuhn–Tucker conditions: −c′(κr)D(κr,pr)+(pr−c(κr))Dκ(κr,pr)+μ1−μ2−μ3Φκ(κr,pr)=0  GraphA14 D(κr,pr)+(pr−c(κr))Dp(κr,pr)−μ3Φp(κr,pr)=0, GraphA15 μ1κr=0, μ2(κr−κ¯), and μ3(Φ(κr,pr)−R)=0. GraphWe denote the unique constrained profit-maximizing product design by (κr,pr) and assume that the carbon constraint is binding so that 0<κr<κ¯ , which implies that μ1=μ2=0 . Substituting Equation A15 into Equation A14 and rearranging yields −cʹ(κr)−zκ(κr;λ)+μ31−F(⋅)[Φp(κr,pr)zκ(κr;λ)−Φκ(κr,pr)]=0. GraphA16The third term on the left-hand side of Equation A16 is negative if Φκ(κ,p)>0 , as Φp<0 and zκ(κr;λ)>0 . Consequently, κr<κ* if Φκ(κ,p)>0 , that is, if the sales expansion from offering a greener product is sufficiently small to not compensate for the direct reduction in overall emissions.Proof of Proposition 7. Suppose that a firm with product design ( κ*,p* ) and profit π* faces regulation that is implemented with probability ρ. If the firm decides to meet the carbon cap by adjusting the product design, the expected reduction in profit is given by π*−[ρπr+(1−ρ)π*]=ρ(π*−πr) , where πr is the constrained optimal profit. Instead, if the firm decides to leave the product design unchanged and to purchase carbon allowances, the expected reduction in profit is given by π*−[ρ(π*−ϖ(Φ(p*,κ*)−R))+(1−ρ)π*]=ρϖ(Φ(p*,κ*)−R) . Clearly, the firm chooses the option that minimizes the negative profit impact. Therefore, the expected cost of a cap-and-trade regulation to the firm is given by ρmin{π*−πr,ϖ(Φ(p*,κ*)−R)} .Proof of Proposition 8. To analyze the impact of a carbon tax, we use the same approach as in Proposition 2. Letting c(κ)≡c(κ)+tκ denote the effective unit cost, the problem in Equation 7 is structurally equivalent to the problem in Equation 4. Therefore, using Equation A5, the profit-maximizing carbon footprint at an interior solution satisfies −cʹ(κ*)=zκ(κ*;λ) , which can be rewritten as −c′(κ*)−t=zκ(κ*;λ). GraphA17Applying the implicit function theorem to Equation A17 yields dκ∗dt=−1c′′(κ∗)+zκκ(κ∗;λ)<0, GraphA18where the inequality follows from the second-order condition. From Equation A7, the first-order condition for the profit-maximizing price reads p*=c(κ*)+[1−F(⋅)] /f(⋅) . Implicit differentiation yields dp∗dt=κ∗f(⋅)2f(⋅)+(p−c(κ∗)−tκ∗)fʹ(⋅)−dκ∗dtzκ(κ∗;λ). GraphA19The corporate carbon footprint can be written as Φ*(t)=κ*(t)[1−F(p*(t)+z(κ*(t);λ))]. GraphDifferentiating Φ*(t) with respect to the tax rate t and substituting for dκ* /dt and dp* /dt given in Equations A18 and A19, respectively, yields Φʹ(t)=dκ*dt[1−F(⋅)]−κ*F(⋅)[dp*dt+zκdκ*dt]  =dκ*dt[1−F(⋅)]−[κ*F(⋅)]22F(⋅)+(p−c(κ*)−tκ*)fʹ(⋅)<0. GraphA20Therefore, a proportional carbon reduces not only the profit-maximizing carbon footprint but also the corporate carbon footprint.The expected cost of carbon taxation is the difference between the actual profit and the expected profit under uncertain taxation, which can be expressed as π*(0)−[ρπ*(t)+(1−ρ)π*(0)]=ρ{π*(0)−π*(t)} , where π*(0)≡π* is the profit under a zero tax rate. Because Dπ*(t) /dt=−κ*D(κ*,p*;λ)=−Φ*(t) from the envelope theorem and using the definition of the corporate carbon footprint, we have that π*(0)−π*(t) =−∫0tdπ*(y)dydy=∫0tΦ*(y)dy, Graphwhich implies that uncertain taxation reduces profit.Proof of Proposition 9. In the absence of carbon regulation, the firm adopts the green technology if π1*−π0*≥f1 . With regulation, the firm adopts the green technology if ρ(π1r−π0r)+(1−ρ)(π1*−π0*)≥f1 . Therefore, if π1r−π0r>π1*−π0* , the threat of carbon regulation captured by ρ>0 relaxes the standard adoption constraint.Proof of Proposition 10. Demand for each firm i, i=1,2 , can be derived from the location of the consumer who is indifferent between buying from firm 1 and firm 2, denoted x^ . From the indirect utility function in Equation 8, this location solves the indifference condition v1(x^)=v2(x^) . With linear mismatch, the consumer located at x^ segments the market, that is, consumers located to the left of x^ purchase from firm 1 , while consumers located to the right of x^ purchase from firm 2 . Demand of firm i can therefore be derived as Di(κ,p;λ)=12−λ(κi−κj)−(pi−pj). GraphA21To illustrate the emergence of competitive carbon offsetting, we focus on the case where c10=c20≡1 and ω < 1 . Firm i then solves maxκi,pi πi(κi,pi)=[pi−(1−κi)2]Di(κ,p). GraphA22The (necessary and sufficient) first-order conditions are given by ∂πi∂κi=2(1−κi*)Di(κ*,p*)+[pi*−(1−κi*)2]∂Di(κ*,p*)∂κi=0 GraphA23 ∂πi∂pi=Di(κ*,p*)+[pi*−(1−κi*)2]∂Di(κ*,p*)∂pi=0. GraphA24Simultaneously solving the first-order conditions of firm i by substituting Equation A24 into Equation A23 and using the properties of demand in Equation A21 (with λ=1 ) yields κi*=12 . The optimal prices pi*=34 are then determined by solving for equilibrium in a standard Hotelling model with given unit costs ci(κi*) and demand. The solution is indeed the profit maximum because πi is concave in both κi and pi and the determinant of the Hessian matrix Hi evaluated at ( κi*,pi* ) is strictly positive (specifically, det(Hi)=2 ). By substitution, x^=12 , πi*=14 (the upper-left cell in Figure 3), and Φi*=14 . Consumer surplus for buyers of firm 1 is obtained as S1(κ1,p1;1)=∫0 (v−p1−κ1−x2−E)dx. GraphA25Because consumers fully internalize their climate externality ( λ=1 ), it follows that E=0 . By substitution, Equation A25 reduces to S1*=8v−1116 , and symmetry implies that S1*=S2* . Welfare is obtained by aggregating consumer surplus and profit net of the climate impact across firms: W*=∑i=12(Si*+πi*−Φi*)=v−118. GraphA26Second, we analyze the setting in which firm 1 uses an offset strategy and firm 2 uses a no-offset strategy. Firm 1 therefore solves maxκ1,p1 π1(κ1,p1)=[p1−(1−κ1)2−ωκ1](12+κ2−(p1−p2)), GraphA27where ω denotes the offset cost per unit of carbon emissions. Instead, firm 2 solves maxκ2,p2 π2(κ2,p2)=[p2−(1−κ2)2](12−κ2−(p2−p1)). GraphA28Using a similar approach as in the case where both firms adopt a no-offset strategy, simultaneously solving the first-order conditions and substituting the unique solutions back into the profit functions yields the optimal profits π^1=1144(ω(ω−4)+9)2≡A and π^2=1144(ω(ω−4)−3)2≡B (the lower-left cell in Figure 3). Note that these profits are reversed in a setting in which firm 1 uses a no-offset strategy and firm 2 uses an offset strategy (the upper-right cell in Figure 3).Third, we analyze the setting in which both firms adopt an offset strategy. Therefore, firm i solves maxκi,pi πi(κi,pi)=[pi−(1−κi)2−ωκi](12−(pi−pj)). GraphA29Using a similar approach as in the previous cases, simultaneously solving the first-order conditions and substituting the unique solutions back into the profit functions yields the optimal profits π¯i=14 (the lower-right cell in Figure 3).Inspection of Figure 3 shows that adopting an offset strategy is a strictly dominant strategy for each firm. The reason is that the offset strategy is more profitable than the no-offset strategy, no matter what the competitor may choose because A>14 and B<14 for all ω<1 .These equilibrium strategy choices are consistent with corporate social responsibility if welfare is improved over the benchmark case where both firm use a no offset strategy. Welfare under offset strategies can be derived as W¯*=∑i=12(Si*+πi*)+(ω−ϕ)∑i=12Φi*−F      =v−ω24−ϕ2(2−ω)−18−F,   GraphA30where (ω−ϕ)∑i=12Φi*−F is the profit of the offset provider.Carbon offsets improve welfare over the case absent offsets if W¯*>W* . Clearly, this holds if the marginal cost ϕ and the fixed cost F are sufficiently small, that is, as long as the offset technology is sufficiently cost effective. " 9,Communication in the Gig Economy: Buying and Selling in Online Freelance Marketplaces," The proliferating gig economy relies on online freelance marketplaces, which support relatively anonymous interactions through text-based messages. Informational asymmetries thus arise that can lead to exchange uncertainties between buyers and freelancers. Conventional marketing thought recommends reducing such uncertainty. However, uncertainty reduction and uncertainty management theories indicate that buyers and freelancers might benefit more from balancing—rather than reducing—uncertainty, such as by strategically adhering to or deviating from common communication principles. With dyadic analyses of calls for bids and bids from a leading online freelance marketplace, this study reveals that buyers attract more bids from freelancers when they provide moderate degrees of task information and concreteness, avoid sharing personal information, and limit the affective intensity of their communication. Freelancers' bid success and price premiums increase when they mimic the degree of task information and affective intensity exhibited by buyers. However, mimicking a lack of personal information and concreteness reduces freelancers' success, so freelancers should always be more concrete and offer more personal information than buyers. These contingent perspectives offer insights into buyer–seller communication in two-sided online marketplaces. They clarify that despite, or sometimes due to, communication uncertainty, both sides can achieve success in the online gig economy.","Online freelance marketplaces, such as Upwork, Fiverr, and PeoplePerHour, have prompted massive transformations in business-to-business (B2B) markets ([13]; [82]). In particular, they allow buyers to post gigs, or short-term service projects, which initiate reverse auctions whereby interested freelance workers submit bids to offer their services ([39]). In these digital environments, buyers and freelancers often devote rather limited time and attention to detailed assessments and instead make choices on the basis of rational value expectations or prices ([ 2]). In addition, online freelance marketplaces suffer from information asymmetries because they rely on text-based messages, which can create uncertainty and hinder the exchange ([34]; [77]). Imagine a buyer wants to hire a freelancer to optimize their pet website's search rankings, so they post a call for bids, requesting ""someone for an SEO job."" In response, Freelancer A might vaguely promise, ""I have plenty of experience writing content that users find interesting to improve the quality and quantity of your traffic,"" whereas Freelancer B more concretely states, ""I have four years of experience writing articles and blogs that engage users and are SEO-friendly. For example, I could focus on interest pieces like the everyday lives of pets."" The communication of both the buyer and freelancer create different degrees of uncertainty, likely impacting who applies and who gets hired.Uncertainty regarding communication can lead to various negative outcomes on both sides, including high rates (more than 50%) of service gigs that go unfulfilled ([36]), diminished bid success, and less-than-optimal pricing for freelancers ([ 2]). However, parties in B2B exchanges can also strategically leverage uncertainty in their communication to achieve more effective outcomes, such as when negotiators conceal information ([69]) or when ambiguous contracts help reduce litigation concerns and increase cooperation ([81]). Buyers and freelancers on online freelance marketplaces engage in a form of B2B exchange, so we propose that they similarly might balance their communication efforts by strategically reducing and increasing uncertainty to maximize their exchange success. In our previous example, by staying vague and without any specific direction from the buyer, Freelancer A might be trying to keep multiple options open and avoid overpromising outcomes.In addition to fundamental questions regarding how to manage uncertainty in B2B exchanges ([50]; [63]), we seek to address the role of communication in such exchanges ([ 7]; Rajdeep et al. 2015; see also Web Appendices A and B). We integrate uncertainty reduction theory ([ 6]) and uncertainty management theory ([ 9]) to predict that, in online freelance marketplaces, various strategies for reducing and increasing the ability of message recipients to anticipate message senders' meaning and actions can benefit the exchange ([ 8]). Using Grice's ([27]) communication principles, we argue that greater provision of task and personal information might reduce uncertainty in service exchanges ([54]) but could also lead to information overload or disagreements ([18]; [40]). Presenting information in a more concrete (cf. abstract) manner or with greater affective intensity also can reduce uncertainty ([27]; [28]; [61]). But again, too much concreteness or affective intensity might lead to restrictive communication that hinders exchanges ([18]; [37]).We apply this theoretical reasoning to exchanges in online freelance marketplaces, in which buyers post calls for bids to attract as many freelancers as possible to apply ([36]). These buyers face a trade-off between reducing uncertainty for freelancers (e.g., providing more information, using less ambiguity) and still efficiently granting them sufficient interpretative freedom. Theorists concur that principles for using relevant information or less ambiguity often get deliberately flouted in conversation, such as when an individual is attempting to save face ([23]) or please a counterpart ([44]). If different communication strategies might entice more freelancers to bid, buyers could establish optimal designs for calls for bids.In response to those calls for bids, freelancers write and submit their bids. In doing so, these freelancers also must manage uncertainty. Thus, they might benefit from matching or mimicking the communication approach adopted by the prospective buyer that issued the call ([78]). Communicative mimicry can evoke similarity perceptions, which tend to increase receivers' sense of rapport and reduce their uncertainty ([75]). Research on adaptive selling recommends matching the buyer's communication (e.g., [57]; [72]). However, in some situations, deviations also may be beneficial ([ 1]), so we consider a more nuanced distinction related to the level at which the similarity occurs. Furthermore, if freelancers compete on price, they may become enmeshed in a self-defeating value trap ([34]; [76]) in which they win more bids but earn less revenue. Strategically mimicking or deviating from a buyer's communication might provide a viable means to winning more gigs without being trapped. We accordingly suggest how freelancers should calibrate their bid formulations to improve their bid success and achieve a price premium.Using a unique, large-scale data set of calls for bids and bids, obtained from a leading online freelance marketplace, along with a series of multilevel models that account for endogeneity, we establish three main contributions. First, we determine the effects of buyers' strategic communications in two-sided online marketplaces ([ 7]). Rather than uncritically recommending that communication should always be informative and unambiguous, we specify the diminishing, even adverse consequences that can result if buyers relay too much task or personal information in a very concrete, intense manner. Second, in an extension of research into adaptive selling ([57]; [78]), we reveal how freelancers' dyadic communicative mimicry affects bid success. Mimicry effects are contingent on the communicative aspect and the buyer's relative uses of each aspect. As we show, mimicking buyers in terms of the provision of task information and use of affective intensity increases bid success. In contrast, we find that freelancers should always offer more personal information and be more concrete in their bid formulations than buyers' calls for bids. Third, we offer insights into how freelancers can avoid predatory pricing ([13]) and escape a value trap ([76]). By strategically managing uncertainty according to the information communicated, and by managing the manner in which they do so, freelancers can earn price premiums. Online Freelance Marketplace ExchangesOnline freelance marketplaces that feature reverse auctions rely on a three-stage process ([34]; [39]). First, in seeking a suitable freelancer, a buyer describes a gig or short-term service project in a call for bids. Second, multiple freelancers apply by formulating and submitting bids that describe themselves, the service offering, and the price requested. Third, the buyer compares the bids and selects a freelancer to complete the project. The outcome of each stage defines exchange success. That is, buyers' success results from a large pool of viable freelance offers. A higher number of bids increases the chances of finding a suitable freelancer for the gig ([35], [36]). Freelancers' success depends on whether their bids are chosen, preferably at a price premium ([13]; [32]). In this context, a price premium is the monetary amount in excess of the buyer's original payment offer (i.e., expected price; [26]; [73]). Buyers might pay a premium beyond their original payment offer for various reasons, including their willingness or ""need to compensate the seller for reducing transaction risks"" ([ 2], p. 248). In competitive online marketplaces, freelancers also might encounter value traps in which they wind up selling more of their services at a lower price ([ 2]; [76]). In this sense, freelancers' success depends on winning the bid but also earning price premiums (or avoiding discounts). Unlike traditional B2B exchanges, buyers' and freelancers' success hinges on textual communication ([13]; [36]). Comparing theories on uncertainty and the role of communication in producing or reducing it, we delineate how both buyers and freelancers may best strike a balance between providing more information and reducing ambiguity versus preserving some uncertainty and maintaining interpretative flexibility. Conceptual BackgroundUncertainty reduction theory ([ 6]) and uncertainty management theory ([ 9]) draw on a central tenet of information theory ([71])—namely, that communication, information, and uncertainty are inextricably linked. Thus, uncertainty is inherent to any interaction. [22] suggests uncertainty depends on the ability to draw inferences from provided information content and the manner in which it is provided. Whereas uncertainty reduction theory predicts how communication can reduce uncertainty, uncertainty management theory examines how people cope with uncertainty, which may include efforts to increase uncertainty to attain beneficial outcomes ([ 8]). Our conceptual development relies on these fundamental principles. Communication Principles in Online Freelance MarketplacesIn online freelance marketplaces, buyers and freelancers depend on one another; all else being equal, they want their mutual exchange to succeed. In such interactions, [27] suggests that four generalized cooperative communication principles (or maxims) apply. Three principles refer to what should be said: the quantity of information (""give as much information as is required and no more than is required""), its quality (""do not say what is false or that for which you lack adequate evidence""), and its relevance. The fourth principle, manner (be clear and avoid ambiguity), pertains to ""how what is said is to be said"" ([27], p. 46). In our study context, neither a buyer nor a freelancer can know upfront whether the other party might be lying, so truthfulness would have to be assumed prior to the exchange. We also highlight that information does not have to be ""correct"" to influence uncertainty perceptions ([ 9]). Therefore, among the four maxims, we focus on the quantity of relevant information that buyers and freelancers offer and the manner in which they present it. Uncertainty Implications of Communication PrinciplesCommunication outcomes are fundamentally uncertain ([ 6]). When people vary their use of communication principles ([27]), they create conversational implications such that message recipients must infer what speakers are trying to imply with their wording. Accordingly, the (un)certainty that buyers and freelancers encounter while making inferences should depend on the degree to which calls for bids and bids provide relevant information in an unambiguous manner, though the meaning of relevant information varies by context. In line with prior research (e.g., [ 7]), we define this degree as the proportion of specific lexical terms used relative to the total number of words in a message.More information reduces uncertainty ([ 6]) and increases receivers' perceptions of the information's value ([79]). In service exchanges, the parties seek information about the task and the person who will complete it ([54]). A greater degree of task information should reduce uncertainty about functional service aspects ([54]). By self-disclosing greater degrees of personal information, a sender also provides a receiver with more information about the self ([15]). In line with the quantity principle ([27]), sparse provision of relevant task and personal information would make it difficult for the receiver to anticipate outcomes or distinguish among options, thus creating uncertainty ([19]).Regarding the principle of manner ([27]), greater degrees of concreteness and affective intensity should reduce ambiguity and enhance clarity. Concrete terms describe something in a perceptible, precise, specific, or clear manner ([11]; [49]; [61]). A greater degree of concreteness reduces ambiguity because it makes it easier for receivers to perceive or recognize what the message sender is implying ([11]; [28]; [61]). Affective intensity reflects the proportion of affective terms included in a message. More affective terms as a proportion of the total word count produce a greater degree of affective intensity, which increases receivers' ability to make evaluative judgments ([28]; [37]).[ 5] We provide examples of these principles in Table 1.GraphTable 1. Communication Elements, Links to Uncertainty, and Examples. Reducing and Maintaining Uncertainty in Communication ExchangesCross-disciplinary research provides ample evidence that conversational partners generally prefer to reduce uncertainty ([ 5]). In B2B relationships, reducing uncertainty increases exchange effectiveness ([29]; [50]; [63]). In Web Appendix A, we offer an overview of some key empirical marketing studies on B2B communication aspects. Specifically in online freelance marketplaces, which are relatively anonymous, the required coordination and dependence between rational buyers and freelancers may increase their need for information and clarity ([13]; [34]). Thus, for example, reputation cues commonly appear in online freelance marketplaces as a way to reduce uncertainty and facilitate exchanges ([34]). More broadly, reducing uncertainty by adhering to [27] principles in dyadic buyer–freelancer communications may boost exchange success.However, people experience uncertainty differently and do not always prefer to reduce it ([ 8]). Instead, according to uncertainty management theory ([ 9]), strategic communication choices that might not minimize uncertainty, and even cultivate it, can be effective and lead to better outcomes for consumers ([38]), organizations ([18]; [31]), and interorganizational governance ([81]). For example, [38] find that a lack of concreteness aids consumers' initial online searches because such vague queries return a greater variety of search results. In collective bargaining settings, seasoned negotiators use concealment and ambiguity to enhance the likelihood of agreement ([69]). In B2B exchanges, parties can use less information and more ambiguity strategically to accomplish specific goals ([ 3]; [81]). Even if such efforts are not universally favored, uncertainty-cultivating communication provides benefits by allowing different receivers to perceive multiple different meanings simultaneously ([18]). Moreover, communication theorists concur that people sometimes deliberately flout or violate [27] conversation principles, such as when they intentionally maintain uncertainty to save face ([23]) or please a counterpart ([44]). Subverting the principles is not necessarily less cooperative, and furthermore, the purpose of communication is not always to be as informative and clear as possible. Arguably, cooperative principles encourage reasonable adherence, not compulsion. Thus, strategically allowing recipients to develop a broader range of possible interpretations by maintaining some level of uncertainty might facilitate buyer–freelancer exchanges. Research Propositions Managing Freelancers' Uncertainty in Calls for BidsFreelancers choose whether to offer their services in response to a buyer's call for bids. The number of freelancers who choose to do so is consequential for the buyer, as more bids implies a greater likelihood of finding a suitable service provider ([36]). Managing freelancers' uncertainty through relevant information provision and the manner of communication in the calls for bids should influence freelancers' decisions to apply. Relevant informationIn calls for bids, buyers can vary the degree of task and personal information included in the description of the gig. If freelancers evaluate this information favorably, they develop more positive dispositions and are more likely to apply ([72]). As prior research establishes, more information enhances communication outcomes in business settings by reducing uncertainty. For example, studying web forums, [79] indicate that the breadth of information provided by a sender affects receivers' objective judgments of the value of that information. [49] find that greater degrees of monetary information increase peer-to-peer lending, and [41] shows that more task information increases the time and commitment sellers allocate to a buyer. Greater degrees of personal information also reduce uncertainty, increase trust ([55]), and enhance performance on crowdsourcing platforms ([68]). Such self-disclosure can strengthen ongoing buyer–seller relations as well ([14]). In contrast, a greater proportion of nonrelevant information (i.e., a lesser degree of relevant information) increases uncertainty ([ 9]). Because greater degrees of task and personal information in calls for bids help reduce freelancers' uncertainty, freelancers who believe they qualify for the gig should be more willing to submit bids.However, excessive relevant information may be ineffective, even if it reduces freelancers' uncertainty. That is, if buyers provide excessive details about the task, the gig may appear too restrictive or prescriptive ([18]), which might not appeal to freelancers. For example, leaving detailed information out of contracts ([21]) or negotiations ([69]) represents a tactic for improving exchange performance. In a downsizing context, a greater degree of information provision can increase uncertainty and negative reactions ([31]). For freelancers, excessive information can feel overwhelming and can limit their motivation, opportunity, or ability to process the information and submit bids ([40]). A buyer that self-discloses a high amount of personal information might also appear less attractive as a prospective business contact ([12]). Because extensive self-disclosures are unusual in initial B2B online exchanges ([46]), such disclosures might be perceived as inappropriate ([59]).In summary, we argue that moderate degrees of task and personal information in calls for bids relate to more freelancer bids. Buyers who provide greater degrees of task and personal information should attract more bids, but beyond a moderate degree (i.e., a very dense provision of relevant information), providing still greater degrees of task and personal information may decrease the number of bids. We thus propose a curvilinear relationship: P1: Extremely sparse and extremely dense degrees of (a) task and (b) personal information in calls for bids yield fewer freelance bids than moderate degrees. Communication mannerIn calls for bids, buyers can vary the concreteness and affective intensity with which they describe the gig. Researchers disagree about whether more or less ambiguous communication leads to more efficacious speech ([ 8]; [18]; [37]), but in an online freelance marketplace, we posit that buyers must reduce ambiguity to at least some extent by being more concrete and intense. Greater concreteness and affective intensity can be more efficient because recipients can process the information with less time and effort ([37]; [61]). These approaches also tend to result in communication that is more persuasive, memorable, and accessible than communication that uses predominantly abstract or unemotional wording ([28]; [37]). In other settings, greater concreteness increases consumer satisfaction with employee interactions and purchase likelihood ([61]). Greater degrees of intensity achieved through proportionally more affective words provide accessible, diagnostic signals to customers ([52]). They can also sway business partners' decisions when used as inspirational appeals ([72]). Finally, greater concreteness and affective intensity provide heuristic cues that allow freelancers to take mental shortcuts, which makes them more likely to bid ([37]).However, if the calls for bids appear too concrete or too intense, the task might appear narrow, which reduces the appeal of performing the gig ([37]). [80] finds that greater vagueness (i.e., less concreteness) can enhance judgments of a speaker's character, message acceptance, and recall. Moreover, some research asserts that reducing uncertainty with more concrete formulations is ineffective ([ 9]; [18]), so managers instead should embrace strategic ambiguity to allow for interpretative freedom ([43]). In contracts, unexpected specificity even increases ex ante costs ([58]). Contrastingly, greater task ambiguity can lower costs as well as reduce the risk of litigation and enhance cooperation in B2B exchanges ([81]). Greater degrees of concrete terms in communications with investors also can have adverse effects ([64]), and excessive degrees of positive affective words diminish the impact of customer reviews ([52]). Thus, we predict a stylistic trade-off: Overly ambiguous calls for bids, lacking any concreteness or affective intensity, may undercut buyers' success in attracting freelancers, but some degree of ambiguity (i.e., avoiding overly concrete, affectively intense communication) can allow for divergent interpretations to coexist. Thus, moderate degrees of concreteness and affective intensity may be most effective in encouraging freelancers to bid. P2: Extremely sparse and extremely dense degrees of (a) concreteness and (b) affective intensity in calls for bids yield fewer freelance bids than moderate degrees. Managing Buyers' Uncertainty in BidsBuyers also face uncertainty when deciding whom to hire and how much to pay ([ 2]; [13]). By managing these uncertainties through their bids, freelancers can affect their chances of winning bids and their price premiums. To establish relevant predictions, we integrate [27] communication principles with uncertainty research such that we anticipate a greater provision of relevant information communicated with greater concreteness and affective intensity allows buyers to draw inferences from freelancers' bids with more certainty. Beyond these communication principles, [ 6] suggest that perceived similarity to a message sender reduces receivers' uncertainty. Thus, both purchase likelihood and buyers' willingness to pay a price premium might be influenced by freelancers' adherence to certain communication principles, as well as by their communicative similarity to the buyer. Winning bidsIn other exchange contexts, research has established that when service employees relay greater degrees of service or personal information ([51] 2015; [62]), it improves customers' intentions to purchase. Willingness to purchase also increases if employees use greater concreteness in online service chats ([61]) or greater degrees of affective words in their emails ([72]).However, the dense provision of relevant information in a bid risks information overload ([40]), and a freelancer being overly concrete or intense might signal a restrictive, narrow approach to the gig ([37]). Our reasoning here parallels that for buyers' formulations of calls for bids. We thus similarly predict that moderate degrees of task and personal information provided in a moderately unambiguous manner (i.e., moderate degrees of concreteness and affective intensity) enhance freelancers' chances of winning the gig.Yet preferences for uncertainty also might be situational and dispositional ([ 9]), as reflected in buyers' own communicative choices ([30]). Specifically, calls for bids can reveal buyers' expectations and preferences for communication behaviors. For example, buyers might like to get to know freelancers, or they may prefer to keep their business relationships impersonal. The extent to which they disclose their own personal information in calls for bids should signal these preferences. An ambiguous bid offered in response to an ambiguous call for bids might lead the buyer to conclude that the freelancer is tactful, sensitive, and noncoercive ([10]). Adaptive communications also raise perceptions of credibility, common social identity, approval, and trust ([52]; [75]), as well as similarity perceptions, all of which in turn reduce uncertainty ([ 6]). Crafting responses that mimic the buyer's communication is a common personal selling recommendation ([78]). As [72] show, when sellers mimic buyers' communicative manner, it increases buyers' attention. Accordingly, freelancers who mimic a buyer's communication content and manner might improve their exchange success.In some situations, though, deviating from buyers' communications may be more beneficial ([ 1]). Even in studies that note the performance benefits of adaption, researchers highlight the importance of the degree of adaptivity (e.g., the degree to which salesperson behaviors adjust for each customer during interactions; [78]). Similarly, studies of communication accommodation investigate the degree of accommodation used ([75]). Extending these insights, the outcomes of adaptation likely depend on communication levels (e.g., very informative vs. not informative). In keeping with uncertainty reduction theory, we expect that buyers are less likely to hire freelancers whose bids offer sparse information and are very ambiguous, even if the call for bids has these characteristics. P3: When the degrees of (a) task and (b) personal information, (c) concreteness, and (d) affective intensity provided by the buyer are at least moderate (sparse), freelancers can increase (decrease) their chances of bid success by mimicking buyers' communications. Achieving price premiumsBuyers' uncertainty about a freelancer should influence their willingness to pay a price premium ([ 2]). Although there are many reasons for price variations ([26]) in online freelance marketplaces, buyers compensate (penalize) freelancers for reducing (increasing) their transaction uncertainty by deciding to accept a price above (below) their original payment offer ([ 2]). In line with [70], freelancers' greater provision of relevant task and personal information in a more concrete and intense manner in bids likely reduces buyers' information asymmetry and exchange-specific risks. Therefore, buyers who want to transact with high certainty may render a price premium for such bids ([51] 2015).The degree to which freelancers mimic buyers' communication also may influence the price premium. For example, [60] find that adaptive approaches for different customers help salespeople increase those customers' willingness to pay a price premium. However, in line with our arguments regarding bid success, we expect that the positive influence of a freelancer's communicative mimicry depends on the specific degree to which the buyer uses a specific communicative element. This reasoning aligns conceptually with the communication principles ([27]), the recommendation that uncertainty should be carefully managed ([ 8]), and the benefits of mimicry identified in studies of communication accommodation ([75]) and adaptive selling ([78]). However, we know of no studies that consider price premium implications of communicative trade-offs between reducing buyers' uncertainty and adapting to buyers' communication. In addition, we are not aware of any research that considers the possible negative effects when sellers mimic buyers who provide less task and personal information, are less concrete, or sparsely use affective intensity.Buyers who want to transact with high certainty might render a price premium to freelancers who reduce uncertainty by providing greater degrees of relevant information in a more concrete and intense manner. But if buyers perceive that the provision of relevant information, degree of concreteness, and level of intensity surpasses their own reasonable level, they might feel overloaded or restricted and thus unwilling to pay a premium. We therefore predict that buyers offer a price premium to freelancers who provide degrees of relevant information, concreteness, and affective intensity at a level similar (but never too sparse) to their own communication, as only these bids help reduce buyers' exchange risks. P4: When the degrees of (a) task and (b) personal information, (c) concreteness, and (d) affective intensity provided by the buyer are at least moderate (sparse), freelancers can increase (decrease) their chances of earning a price premium by mimicking the communication of the buyer.Graph: Figure 1. Effect of buyers' communication on call for bids success. Field Study of an Online Freelance Marketplace Setting and SampleWe conducted a large-scale field study with a proprietary data set of calls for bids and corresponding bids posted on a leading, global online freelance marketplace. The marketplace hosts seven freelance service submarkets: ( 1) design; ( 2) writing and translation; ( 3) video, photo, and audio; ( 4) business support; ( 5) social media, sales, and marketing; ( 6) software and mobile development; and ( 7) web development. The bidding process follows a sequential, sealed-bid reverse auction format, and it concludes when the buyer chooses one winning bid ([34]; [39]). As with recent marketing research that investigates large scales of communication (see Web Appendix B for an illustrative overview), this process depends on and is captured in text data. We used ( 1) text data from 343,796 calls for bids issued by 49,081 buyers (restricted to those who posted at least two gigs) to predict buyers' call for bids success, ( 2) 2,327,216 bids submitted by 34,851 freelancers (restricted to those who submitted at least two bids) to predict freelancers' bid success, and ( 3) 148,158 bids submitted by 30,851 freelancers (restricted to those who won and for which the payment was disclosed) to predict freelancers' price premium. Our multilevel approach required more than one observation (call for bid or bid) in each Level 2 unit (buyer or freelancer); otherwise, Level 2 and Level 1 variance might have been confounded ([74]). Web Appendix C summarizes the definitions and operationalizations, and Web Appendix J provides the descriptive statistics and correlations. Measurement of ConstructsThe number of freelancers who submit bids to offer their services provided the measure of success of buyers' call for bids. More submitted bids increases the probability that buyers can find an appropriate freelancer, whereas failing to find a suitable match is time consuming and costly because it requires further searches and delays the project ([35], [36]). We measured freelancers' bid success as a binary indicator of whether ( 1) or not (0) the freelancer was chosen by the buyer and won the bid ([32]). For freelancers' price premium, we gauged the percentage by which the accepted bid price for the project exceeded (or fell short of) the buyer's original payment offered (i.e., benchmark price; [20]). This operationalization accounted for the difference between the final price a buyer paid and the original price they offered (i.e., what the buyer expected to pay) ([73]).To capture the independent communication variables, we mined the text of each call for bids and each bid. For the preprocessing and extraction steps, we used the R package Quanteda ([ 4]), as well as a combination of newly developed and prevalidated text mining dictionaries. For the degree of task information in each text, we inductively sourced a list of context-specific task descriptor words. To start, we acquired all 34,851 freelancers' service skill tags ([ 7]; for an illustration, see Web Appendix D), which freelancers list in their profiles to describe the service tasks they offer (e.g., ""developer,"" ""illustrator""). After removing stop words and duplicates, two coders reviewed the remaining word list, deleted any misspelled words, and removed terms that did not describe a service (e.g., ""great,"" ""reliable""). Using Quanteda ([ 4]), we stemmed the remaining words, leaving 1,912 unique word stems that describe service tasks. We mined each call for bids and bid, then summed word occurrences reflecting the new task dictionary. By dividing this sum by total words, we obtained a measure of the degree (ratio) of task information in each text. When people self-disclose personal information, they use singular, first-person pronouns. In line with previous research (e.g., [67]), we measured the degree of personal information as the ratio of first-person singular pronoun words (e.g., ""I,"" ""me"") to the total words in each text. To determine the degree of communication concreteness, we mined each text for [11] list of generally known English lemmas that indicate whether a concept denoted by a term refers to a perceptible entity. Following their operationalization, we included all terms that received a rating of 3 or greater on their bipolar, five-point abstract-to-concrete rating scale.[ 6] That is, terms that score 3 or higher refer to relatively more specific objects, materials, people, processes, or relationships. We again divided the sum of the concrete terms by the total words in each text. Finally, the ratio of emotion-laden words (e.g., ""problematic,"" ""easy""; [28]; [37]) determined affective intensity. Using the Linguistic Inquiry and Word Count (LIWC) affect dictionary, we obtained a list of affect words, which we then summed for each text ([66]) and divided each by the corresponding total word count to obtain the degree of affective intensity. Pilot Studies Validity of text-mined measuresTo ensure the validity of our text-mined communication measures, we asked two coders to classify the texts of a random subsample of 100 calls for bids (Mlength = 129 words) and 100 bids (Mlength = 102 words). The coders indicated whether considerable task information, personal information, concreteness, and affective intensity were present in each text ( 1) or not (0). Comparing the coders' classifications with our text-mined classification revealed substantial agreement for both calls for bids (.73 to.94) and bids (.66 to.88) ([47]). The average F1 measure was sufficiently high for both bids (.79 to.95) and calls for bids (.80 to.95), as we detail in Web Appendix F. Experimental evidence of uncertainty reductionTo establish the internal validity of the chosen communication aspects on receivers' uncertainty perceptions, we conducted a series of experimental pilot studies. We used single-factor, within-subject designs for ( 1) task information, ( 2) personal information, ( 3) concreteness, and ( 4) affective intensity. For each pilot study, we recruited between 50 and 53 U.S. consumers with a mean age of 37.6 years (50% women) from Amazon Mechanical Turk (for details, see Web Appendix G). In line with previous research (e.g., [28]; [49]; [55]; [61]), we find that greater use of all four communication aspects in bids significantly reduces buyers' uncertainty perceptions and affects their hiring intentions. Predicting the Success of Buyers' Calls for Bids Model-free evidenceIn Web Appendix H, we summarize the model-free findings. The mean-level comparison indicates that calls for bids with significantly greater degrees of task information and concreteness, as well as significantly lower degrees of personal information and affective intensity, receive more freelance bids than an average call for bids (M = 5). Econometric model and identificationThe success of calls for bids reflects a count variable. Noting the overdispersion in the data (p < .001), we used a negative binomial model instead of a Poisson model. Furthermore, calls for bids are nested within buyers, and thus, the call for bids and number of freelancers who offer their service might be interdependent. The significant between-group variance (p < .001) and ICC( 1) of.27 suggests a multilevel structure. We therefore specified a multilevel model with a random intercept to control for time-invariant unobserved differences between buyers (e.g., education, country, gender) that could relate to differences in their success, using the following base equation: CALSUCij=y00+y01BTASKij+y02BPERSij+y03BCONCij+y04BINTEij+y05BTASK_SQij+y06BPERS_SQij+y07BCONC_SQij+y08BINTE_SQij+μ0j+εij, Graph( 1)where CALSUCij is the success of a call for bids i (i = 1, ..., 343,796) issued by buyer j (j = 1, ..., 49,081), BTASKij is buyer task information, BPERSij indicates buyer personal information, BCONCij is buyer concreteness, and BINTEij refers to buyer affective intensity in the call for bids. In turn, BTASK_SQij is buyer task information squared, BPERS_SQij is buyer personal information squared, BCONC_SQij is buyer concreteness squared, and BINTE_SQij is buyer affective intensity squared. Finally, μ0j is the random intercept and εij is the error term.Some empirical challenges inhibited a robust model identification, which we addressed in several ways. To account for observed heterogeneity, we incorporated covariates that might influence how many freelancers respond to a particular call for bids. First, in line with extant text mining studies ([ 7]), we controlled for the word count in each call for bids. Second, as a reputation cue, we measured buyer experience as the number of projects a buyer had commissioned previously on the platform prior to posting the focal call for bids ([32]). Third, a higher payment offer may attract more freelancers ([36]), so we determined the payment offered by the buyer in U.S. dollars, multiplied by an undisclosed index for anonymity. We used a dummy for nondisclosed payments, but we replaced missing values with a grand mean to retain the observations. Fourth, we measured project duration, as longer projects attract more freelancers ([36]). A dummy variable indicated whether the project was slated to last more ( 1) or less than a month (0). Fifth, more buyers demanding freelance services at the same time creates a relative shortage of freelancers ([36]). To account for an excess supply of freelancers, we calculated the sum of all active freelancers in the specific submarket of the call for bids and divided by the sum of all calls for bids posted around the same time (±31 days) in the same submarket. Sixth, the marketplace grew over time, so we included fixed effects for the year of the call for bids. Seventh, we included fixed effects for the seven submarkets, since submarkets that feature more complex projects have fewer qualified freelancers.Beyond these observed covariates, buyers' bid formulations might have varied by project characteristics unobservable to us. To the extent that these unobserved project characteristics influenced both the buyers' communication strategies and buyer outcomes, the estimated parameters might be biased. Therefore, we concatenated all service skill tags from the service profile of each freelancer who submitted a bid in response to a specific call. Then, to uncover the latent mixture of project types, we applied a latent Dirichlet allocation model to the project-specific skill tags (e.g., [ 7]; see Web Appendix I). We included the resulting 12 latent project characteristics as fixed effects to account for unobserved heterogeneity.Buyers also strategically make their communication decisions in learned anticipation of a larger number of bids or other factors, which were potentially unobservable to us. This strategic behavior could make communication approaches endogenous ([42]). Because our data did not contain valid, strong instruments for buyers' communications, we adopted [65] approach and used Gaussian copulas to model the correlation between each buyer communication BCOMij1-4 and the error term. We added regressors to Equation 1, such that BCOMij1−4~=Φ−1[H(BCOMij1−4)], Graph( 2)where Φ−1 is the inverse of the normal cumulative distribution function and [H(BCOMij1−4)] represents the empirical distribution functions of the four buyer communication approaches. The endogenous regressors must be nonnormally distributed for identification ([65]), and we confirmed this was true using Shapiro–Wilks tests (all p < .001). The updated equation to predict buyers' call for bids success, after correcting for endogeneity, was thus CALSUCij=y00+y01BTASKij+y02BPERSijqquad+y03BCONCij+y04BINTEijqquad+y05BTASK_SQij+y06BPERS_SQijqquad+y07BCONC_SQij+y08BINTE_SQijqquad+y09−14CONij1−6+y15−20YEARij1−6qquad+y21−26SUBMij1−6+y27−37PROJij1−11qquad+y38−41BCOM~ij1−4+μ0j+εij, Graph( 3)where CONij1−5 is the vector of control variables, YEARij1−6 are year effects, SUBMij1−6 are submarket effects, PROJij1−11 are latent project clusters, and BCOM~ij1−4 are Gaussian copulas. We used a robust estimator to account for correlated and clustered standard errors. Results and discussionThe maximum variance inflation factor is 2.11, indicating no potential threat of multicollinearity. Table 2 contains the results of a main effects model and the full model, and Figure 1, Panels A–D, display the curvilinear effects from the full model. We have proposed that extremely sparse and extremely dense degrees of relevant information, concreteness, and affective intensity in calls for bids yield fewer freelance bids than moderate degrees of these communication elements. In line with our expectations, we find a positive linear effect (.152, p < .01) and negative squared effect for task information (−.026, p < .01), as displayed in Figure 1, Panel A. Moderate levels of the use of task information (50%:.222, p < .01) yield better results than sparse (10%: −.426, p < .01) and dense (90%: −.495, p < .01) uses. Furthermore, we find a positive linear effect (.052, p < .01) and negative squared effect for concreteness (−.080, p < .01) (Figure 1, Panel C). Moderate use (50%:.078, p < .01) yields better results than sparse use (10%: −.092, p < .01) or dense use (90%: −.251, p < .01) of concreteness. Contrary to our expectations, we find a negative linear effect (−.190, p < .01) and a positive squared effect (.032, p < .01) of personal information (Figure 1, Panel B). We also find a negative linear effect (−.084, p < .01) and a nonsignificant squared effect (.001, ns) of affective intensity (Figure 1, Panel D). Thus, it appears that any provision of personal information or greater use of affective intensity by the buyer is always ineffective. As a possible explanation, we note that in B2B online conversations, self-disclosure and emotions may be valued only after business relations have been established, not at the moment they form ([46]). Most of the exchanges in our data were between strangers, rather than being repeat exchanges, so it may be more appropriate for buyers to avoid personal details and appear rational rather than emotive.Graph: Figure 2. Response surfaces for bid success and price premium.GraphTable 2. Predicting the Success of Buyers' Calls for Bids. 1 Notes: Standardized results. Significance is based on two-tailed tests. The dependent variable is the count of all bids received. The sample included all projects listed by buyers with at least two projects to which at least one freelancer submitted a bid. Effects for years, submarkets, project characteristics, and Gaussian copulas are detailed in Web Appendix Q.2 *p < .05.3 **p < .01.To entice more freelancers to bid, buyers should keep their calls for bids brief (−.027, p < .01 for word count), which emphasizes the need for careful formulations. Higher payment offers (.168, p < .01), longer project durations (.117, p < .01), and an excess supply of freelancers (.606, p < .01) all increase the number of bids. Notably, the number of projects a buyer has previously commissioned relates negatively to the number of freelancers who bid (−.033, p < .01). These experienced buyers might have established relationships with specific freelancers, which reduces other freelancers' chances and causes them to refrain from bidding ([48]). Predicting Freelancers' Bid Success Model-free evidenceBids that offer less personal information and greater task information, concreteness, and affective intensity are more successful in winning projects. Among bids that won, the mean-level comparisons indicate nonlinear effects of mimicry. That is, successful freelancers mimic buyers' use of task information, personal information, and concreteness closely. If a buyer uses very sparse or very dense degrees of these communication aspects, the winning freelancers deviate more, indicating a nonlinear impact of mimicry. We do not find evidence of this mimicry relationship for affective intensity (see Web Appendix H). Measurement of similarityPrevious studies often operationalize communication similarity as the absolute difference between two measures (e.g., [52]; [75]), but this approach suffers some implicit constraints ([17]). In particular, difference scores suggest that one party's communication increases at the same magnitude as the other's decreases. They also ignore the degree at which the relative mimicry occurs. As a preferable alternative, we use polynomial regression, which allows for simultaneous testing of similarity and dissimilarity effects on bid success, at different levels of freelancers' and buyers' uses of the four communication aspects. In their study of positive and negative emotional tone convergence, [24] also use polynomial regression to explore the nuanced effects of convergence in leader–follower relationships on leader–member exchange quality. A simple regression model that captures absolute deviation cannot simultaneously assess the degree of task information by the buyer and the potential nonlinear effects of task information mimicry by the freelancer. So, we performed a polynomial regression with response surface analyses for each communication aspect to capture the extent to which freelancers mimicked a prospective buyer's provision of relevant information and communication manner. We detail this polynomial modeling approach that led to Equation 4 and the calculation of all polynomial terms, using task information as an example, in Web Appendix E. Econometric model and identificationWe tested freelancers' trade-off between adding more uncertainty-reducing communication versus mimicking the buyer's communication in a polynomial regression model that included linear terms, quadratic terms, and interactions. In the multilevel base equation to predict freelancers' bid success (ICC( 1) = .09, p < .001), BIDSUCkl=y00+y01−04FCOMkl1−4+y05−08BCOMkl1−4+y09−12FCOM_SQkl1−4+y13−16(FCOMkl1−4×BCOMkl1−4)+y17−20BCOM_SQkl1−4+μ0l+εkl, Graph( 4) BIDSUCkl is the success of bid k (k = 1, ..., 2,327,216) by freelancer l (l = 1, ..., 34,851), FCOMkl1−4 are the four freelancer communication aspects, BCOMkl1−4 indicate the four buyer communication aspects, FCOM_SQkl1−4 are freelancer communication aspects squared, (FCOMkl1−4×BCOMkl1−4) are interactions of freelancer and buyer communication aspects, BCOM_SQkl1−4 are buyer communication squared, μ0l is the random intercept, and εkl is the error term.We incorporated several covariates that might influence freelancers' bid success. As in the buyer model, we controlled for word count, project payment, project duration, and excess supply of active freelancers. We also included fixed effects for years, submarkets, and latent project characteristics. We accounted for the number of projects the freelancer completed prior to submitting the focal bid as a reputation cue that might determine bid success ([32]). Freelancer rating is an average five-point satisfaction rating that a freelancer has received for all completed projects. To retain observations of unrated freelancers, we included a dummy for observations without star ratings and replaced the missing values with a grand mean rating.Several additional controls relate to whether a bid is successful. First, following prior research, we assessed linguistic style matching, or the similarity between each bid and the respective call for bids, across nine function word categories ([52]). Second, we accounted for any previous relationship in which the freelancer had completed at least one project for the same buyer prior to the specific call for bids ([32]). Third, freelancers submit a bid price that may differ from the payment offered by the buyer, and a higher bid price may reduce the likelihood of bid success ([32]). In light of this, we measured each bid price as a ratio between the asking price and the average indexed bid price requested by all competing freelancers for the same call for bids. Fourth, the longer it takes freelancers to submit a bid, the lower their chances of success ([34]). So, we measured time-to-bid as the number of days between the posting of the call for bids and the bid submission. A dummy variable also indicates whether the bid was submitted late ( 1) or on time (0). Fifth, competition for a specific call for bid should impact each bid's success chances, so we controlled for the number of bids for the same call ([34]).Similar to buyers, freelancers make communication decisions strategically in anticipation of higher bid success or other, unobservable factors. Thus, freelancer communication is potentially endogenous, so we again used Gaussian copulas (Shapiro–Wilk tests: all p < .001). The updated equation to predict freelancers' bid success is as follows: BIDSUCkl=y00+y01−04FCOMkl1−4+y05−08BCOMkl1−4+y09−12FCOM_SQkl1−4+y13−16(FCOMkl1−4×BCOMkl1−4)+y17−20BCOM_SQkl1−4+y21−33CONkl1−13+y34−39YEARkl1−6+y40−45SUBMkl1−6+y46−56PROJkl1−11+y57−60FCOM~kl1−4+y61−64BCOM~kl1−4+μ0l+εkl, Graph( 5)where CONkl1−14 is the vector of control variables, YEARkl1−6 are year effects, SUBMkl1−6 are submarket effects, PROJkl1−11 are latent project clusters, FCOM~kl1−4 are Gaussian copulas for bid text, and BCOM~kl1−4 are Gaussian copulas for calls for bids text. Results and discussionThe maximum variance inflation factor is 3.86, indicating no threat of multicollinearity. Table 3 contains the results of the freelancer bid success models, Web Appendix K summarizes the response surface coefficients, and Figure 2 displays these coefficients on three-dimensional surfaces, reflecting relationships among freelancer communication, buyer communication, and bid success. We also highlight the misfit line used to explore the trade-off between exceeding and falling short of buyers' communication levels.GraphTable 3. Predicting Freelancers' Bid Success. 4 Notes: Standardized results. Significance is based on two-tailed tests. The dependent variable is whether the freelancer was chosen and won the bidding process. The sample included all bids by freelancers with at least one winning and at least one losing bid. Effects for years, submarkets, project characteristics, and Gaussian copulas are detailed in Web Appendix Q.5 *p < .05.6 **p < .01.We have proposed that when the degree of relevant information, concreteness, and affective intensity provided by the buyer is at least moderately dense (sparse), freelancers can increase (decrease) their chances of bid success by mimicking the buyer's communication. The surface-level tests along the plotted misfit line (Web Appendix K) display negative curvatures for task information (−.020, p < .01), personal information (−.007, p < .01), concreteness (−.011, p < .01), and affective intensity (−.020, p < .01). These results indicate that mimicking the buyer's communication increases bid success (see Web Appendix L for further clarification).In line with our proposition, we qualify this effect for sparse degrees of task and personal information, concreteness, and affective intensity provided by the buyer in Web Appendix M. If we were to find positive slope coefficients at lower levels, it would suggest that freelancers can increase their chances of bid success by exceeding, rather than mimicking, the buyer's communication. This prediction holds for personal information (.020, p < .01) and concreteness (.024, p < .01), according to the slopes at low levels of buyer communication. However, contrary to our expectations, we find negative effects for the slopes of task information (−.008, p < .01) and affective intensity (−.030, p < .01) at low levels of buyer communication. Therefore, freelancers should always mimic the degree of task information and affective intensity provided by the buyer. For these two communication aspects, the tenets of communication accommodation theory ([75]) and adaptive selling ([78]) hold: mimicking the buyer is always better. To increase their chances of bid success further, freelancers also must keep their bids concise (−.021, p < .01 for word count). Reputation cues (experience:.002, p < .01; rating:.010, p<.01) increase freelancers' chances of bid success, as do linguistic style matching (.051, p < .01), previous business relations with the buyer (.078, p < .01), lower bid prices (−.006, p < .01), timely (cf. late) bid submissions (−.004, p<.01), lack of competition (−.251, p < .01), and reduced supply of freelancers (−.042, p < .01). Predicting Freelancers' Price Premium Model-free evidenceBids with significantly less affective intensity and significantly more task information, personal information, and concreteness achieve greater price premiums than an average bid (M = 14% discount). Moreover, 96% of freelancers completed projects without any price premium, indicating the prevalence of value traps. The bids that achieved price premiums mimicked those buyers that made moderate use of task information, concreteness, and affective intensity closely, yet they deviated from buyers that made very sparse or very dense use of them. For personal information, we find a distinctive, positive, linear relationship for mimicry. Successful freelancers mimicked buyers that supplied a lot of personal details but deviated if buyers supplied very little or moderate degrees of personal information (Web Appendix H). Econometric model and identificationThe price premium analysis is restricted to bids that win and buyers that disclose their payment offer upfront. Thus, our estimates may be biased by buyers' self-selection, in terms of which bid they chose and whether they disclosed payments. Therefore, we employed a two-stage selection model. In the first stage, we estimated a choice model, with the availability of the necessary data as a binary dependent variable (i.e., bid was won and payment was disclosed). Using this model, we computed the inverse Mills ratio to account for the potential selection bias (probit model in Web Appendix N) and included this correction term in the final model estimation. To identify second-stage parameters, there needed to be one term in the first-stage equation that was unrelated to the error term in the freelance price premium equation. We thus included the dummy that indicates if the bid was submitted late only in the first-stage equation because this term explained buyers' choice of the bid, but we did not expect it to be conceptually related with the eventual price premium. Thus, this term satisfied both relevance and exogeneity requirements. The updated equation of our multilevel model (ICC( 1) = .13, p < .001) is as follows: PREMIUMkl=y00+y01−04FCOMkl1−4+y05−08BCOMkl1−4+y09−12FCOM_SQkl1−4+y13−16(FCOMkl1−4×BCOMkl1−4)+y17−20BCOM_SQkl1−4+y21−31CONkl1−11+y32−37YEARkl1−6+y39−43SUBMkl1−6+y44−54PROJkl1−11+y55−58FCOM~kl1−4+y59−62BCOM~kl1−4+y63IMRkl+μ0l+εkl, Graph( 6)where PREMIUMkl is the price premium of bid k (k = 1, ..., 148,158) offered by freelancer l (l = 1, ..., 30,851), and IMRkl is the correction term. Results and discussionThe maximum variance inflation factor is 2.74, indicating no threat of multicollinearity. Table 4 contains the results of the freelancer price premium models, Web Appendix K details the response surface coefficients, and Figure 2 displays the surfaces.GraphTable 4. Predicting Freelancers' Price Premium. 7 Notes: Standardized results. Significance is based on two-tailed tests. The dependent variable is price premium for the chosen bid. The sample includes all winning bids for which the payment was disclosed. Effects for years, submarkets, project characteristics, and Gaussian copulas are detailed in Web Appendix Q.8 *p < .05.9 **p < .01.We proposed that when the degree of relevant information and communication manner provided by the buyer is at least moderately high (low), freelancers increase (decrease) their chances of earning a price premium by mimicking this communication. Web Appendix O displays the misfit lines on two-dimensional planes. In line with our expectations, the surface-level tests along the plotted misfit line show a negative curvature for task information (−.023, p < .01), concreteness (−.007, p < .01), and affective intensity (−.008, p < .01), such that mimicking the buyer's communication increases bid success. However, for personal information, we find a positive curvature (.003, p < .05), which implies freelancers should always offer more personal information than the buyer. For these B2B services, the provider and the service are inseparable, which may lead buyers to place more value on personal information about freelancers, even if their own provision of personal details in the calls for bids is sparse.If a buyer provides little relevant information or is less concrete (Web Appendix P), a positive slope would suggest that freelancers can increase their chances of earning a price premium by exceeding rather than mimicking the buyer. We find support for this prediction in the slope of personal information (.027, p < .01) at low levels of buyer personal information. However, negative effects emerge from the slopes of task information (−.016, p < .01) and affective intensity (−.012, p < .01), and we find a nonsignificant effect for concreteness (.002, n.s.). Mimicking the buyer's task information and affective intensity is always better, which is in line with accommodation theory and adaptive selling ([75]; [78]).Freelancers also increase their price premiums by avoiding lengthy bids (−.014, p < .01). Although platform reputation cues (experience and rating) can boost freelancers' chances of bid success, they do not determine the final price buyers pay. The skew in the ratings toward very high scores may limit their ability to help prospective buyers determine an appropriate price ([45]. Linguistic style matching (.023, p < .01), a previous relationship with the prospective buyer (.056, p < .01), submitting early in the bid process (.009, p < .01), and reduced competition (−.015, p < .01) all increase buyers' acceptance of a price premium. General DiscussionAcross disciplines, substantial research has identified various success determinants in online freelance marketplaces (e.g., [36]; [77]). For example, studies of B2B exchanges and two-sided marketplaces emphasize communication (see Web Appendix A). But at the specific word level, we lack insights into the optimal information or manner of communication ([ 7]). With this initial investigation of how buyers' and freelancers' success might be enhanced by appropriately managing the other party's uncertainty, we postulate, in line with uncertainty reduction ([ 6]) and uncertainty management ([ 9]) theories, that communication that is not completely informative and clear may still be effective. Accordingly, we investigate how buyers' communication can attract freelance bids and how freelancers' communication can determine their bid success and price most effectively, and the results offer both theoretical and practical implications. Theoretical ContributionsFirst, we advance research on how buyers' communication determines their ability to attract freelancers. Drawing on prior communication research, we identify communication principles that critically relate to receivers' uncertainty, such as relevant task and personal information and the relative concreteness and affective intensity with which this information is communicated ([ 8]; [27]). To entice more freelancers to bid, buyers should carefully formulate their calls for bids to keep them brief. Freelancers' information processing motivation, time, skills, and proficiency likely are limited, so buyers must choose their wording carefully and select from various effective communicative aspects. They can attract a larger pool of bids if they provide moderate degrees of task information in a moderately concrete manner. Offering too little of these features leaves freelancers with too much uncertainty, and dense information provision or being very concrete is too restrictive. If buyers provide greater degrees of personal information or express greater affective intensity in their calls for bids, it reduces the number of service offers they receive. This finding contrasts with uncertainty reduction theory ([ 6]) and B2B research that suggests self-disclosure strengthens buyer–seller cooperativeness ([41]). However, instead of ongoing B2B relationships, our study refers mostly to initial interactions between strangers (in 98% of cases, the freelancer had never worked for the prospective buyer). Evidence obtained from buyer–seller online chats similarly suggests that self-disclosure and emotive expressions are valued only in existing B2B relationships, not in new ones ([46]). Overall, we offer empirical support for communication theorists' suggestions that common communication principles can be purposefully flouted to achieve better conversation outcomes ([23]).Second, freelancers must keep their bids concise. They too face a trade-off between reducing the buyer's uncertainty and offering overly dense information. In line with research on communication accommodation ([75]) and adaptive selling ([78]), we show that freelancers can improve their bid success by mimicking the prospective buyer's communication. Adding to these research streams, we introduce a contingency perspective that reveals the efficacy of mimicry depends on the degree to which buyers use specific communication elements. In line with accommodation theory and adaptive selling, bid success always improves when freelancers mimic buyers' provision of task information and use of affective intensity. However, in line with uncertainty reduction theory ([ 6]) and expectancy violations ([ 1]) when buyers supply little personal information and are less concrete, freelancers can increase their chances of bid success by diverging and providing more personal information and concreteness.Third, freelancers often struggle to avoid value traps in which they sell more of their services for less ([76]). Rational buyer expectations should allow high-quality freelancers to charge price premiums ([70]), but the quality of freelance services is unobservable prior to purchase, and rational buyers might refuse to pay any price premium if they feel uncertain and suspect the freelancer may be hiding information ([16]). Therefore, to achieve premiums, freelancers should offer short, appropriately formulated bids. Buyers are more willing to pay a premium to freelancers who mimic their provision of task information, concreteness, and affective intensity, which is in line with communication accommodation theory ([75]) and adaptive selling research ([78]). However, similar to the findings for bid success, freelancers should offer more personal information than buyers, rather than mimicking buyers' provision of such information. In most service settings, a ""bad"" seller might provide a great product by chance; however, almost by definition, a bad freelancer produces bad service ([36]). This tight coupling between the freelancer and service quality represents a conceptual distinction in our study, which accordingly shows that buyers' willingness to pay a premium increases with more personal information issued by the freelancer. Practical ImplicationsOur findings offer actionable insights for the millions of buyers and freelancers utilizing online freelance marketplaces, the collective value of which is predicted to reach $2.7 trillion by 2025 ([56]). In detail, being informative and unambiguous may be a common assumption, but it is not an imperative, nor does it always lead to success. Implications for buyersAlthough 59% of U.S. companies use a flexible workforce to some degree, more than one-third of contracted projects are never completed ([33]). To attract freelancers, buyers should keep their calls for bids succinct. Beyond that recommendation, we offer several tips for formulating calls for bids in Table 5. In particular, a task description with a moderate amount of information helps freelancers anticipate the task without overloading them with details. Due to the relative anonymity of online freelance marketplaces, buyers might assume that freelancers will need to know who they are, but instead, we find that the less buyers describe themselves (to focus on describing the task), the better the outcomes. Relatable and imaginable (rather than abstract) descriptions of the project help freelancers grasp the requirements. However, being excessively concrete becomes prescriptive, which deters freelancers. Using emotion words makes the content of a call for bids relatively more intense. Such intensity can remove ambiguity and make opinions quickly accessible, but we find that calls for bids are more effective if they are formulated relatively impassively. Enthusiastic project descriptions seemingly might raise freelancers' suspicion that the project is too good to be true. Also, offering higher payment might attract a larger pool of freelance bids, as do long- rather than short-term gigs. Finally, more freelancers bid when there are fewer calls for bids in the subsector.GraphTable 5. Buying and Selling Services in Online Freelance Marketplaces. 10 Notes: Web Appendix S provides the full call for bids and bid examples we used for calculating the degrees of each communicative principle and the corresponding expected lift success. We used the ""good practice"" call for bids example to devise the bad and good examples for the corresponding freelance bid. Implications for freelancersFreelancers are not necessarily natural marketers, but their bid formulations determine their marketability. Existing online reputation systems provide some assistance, but they also create entry barriers to new freelancers who first must earn good overall ratings ([13]). Fortunately, winning gigs and achieving price premiums also depends on freelancers' communication. Table 5 includes advice to help freelancers formulate more successful bids and avoid the value trap. In line with the mantra of adaptive selling, the call for bids provides a starting point in which mimicking the buyer's task information and affective intensity increases freelancers' success—even if they provide few task details or seem very impassive. But freelancers should always offer personal information and be concrete. Even if a buyer does not provide personal information or the call is relatively abstract, freelancers' chances of success and obtaining price premiums increase if their bids contain more personal information and are at least somewhat concrete. The strongest predictor of bid success is a preexisting buyer relationship, so more broadly, freelancers should grow their buyer relations. Limitations and Directions for Further ResearchIn examining theoretically grounded communicative aspects, we offer novel insights into how to manage uncertainty in buyer–freelancer exchanges. Intriguingly, we find that communication approaches that do not aim to minimize uncertainty can be effective. Continued research should investigate this notion further and develop additional insights into the exchange implications of linguistic choices in B2B but also B2C and C2C communication on multisided platforms ([53]). For example, affiliative ([66]) or collaborative terms might affect uncertainty and influence exchanges as well. Arguably, the personal characteristics of buyers and freelancers (e.g., gender, education, experience), channel choices ([50]), different sources of uncertainty ([29]), perceived risks ([25]), and spatial distances between buyers and freelancers also might moderate the efficacy of communication aspects, so additional research should specify their influences. For example, if buyers lack the expertise to specify what they want, they might benefit from more ambiguous calls for bids ([38]). Perhaps buyers' communication or alternative factors that we cannot account for (e.g., underestimation of the amount of work required to fulfill the task) influence the final price they pay, too. Efforts to specify these additional effects also might address some of our more controversial findings, such as the evidence that the number of previously commissioned projects by a buyer relates negatively to the number of freelancers who bid. We posit that experienced buyers might prefer freelancers whom they have hired in the past ([48]). Buyers also might have incurred switching costs or supplier dependencies ([29]). Methodologically, we estimated all the models sequentially, as buyers' calls for bids and their success occur prior to freelancers' bids and their success. But an equilibrium approach that estimates these models simultaneously at the bid level could reflect an alternative way to think about the data structure. The concreteness word list we used ([11]) may also require further refinement to differentiate specific concreteness levels among the word set. Finally, the anonymity and speed of exchanges in online freelance marketplaces may make communication particularly important in this context. A comparative analysis of the influence of uncertainty management efforts across different B2B contexts beyond these marketplaces could offer interesting insights, especially if uncertainty avoidance is a central goal. " 10,Conducting Research in Marketing with Quasi-Experiments," This article aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing. The purpose of quasi-experimental methods is, in the absence of experimental variation, to determine the presence of a causal relationship. First, the authors explore how to identify settings and data where it is interesting to understand whether an action causally affects a marketing outcome. Second, they outline how to structure an empirical strategy to identify a causal empirical relationship. The article details the application of various methods to identify how an action affects an outcome in marketing, including difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The authors emphasize the importance of clearly communicating the identifying assumptions underlying the assertion of causality. Last, they explain how exploring the behavioral mechanism—whether individual, organizational, or market level—can actually reinforce arguments of causality.","Quasi-experimental methods have been widely applied in marketing to explain changes in consumer behavior, firm behavior, and market-level outcomes. ""Quasi-experiment"" refers to the use of an experimental mode of analysis and interpretation to data sets where the data-generating process is not itself intentionally experimental ([23]). Instead, quasi-experimental research uses variation that occurs without experimental intervention but is nonetheless exogenous to the particular research setting. Work using quasi-experiments in marketing settings has used events such as weather, geographic boundaries, contract changes, shifts in firm policy, individual-level life changes, and regulatory changes to approximate a real experiment. In each case, an external shock creates a source of exogenous variation that the researcher uses to establish a causal relationship between the variation and the outcome of interest.Companies also use quasi-experimental methods to understand the consequences of key business actions. For example, [17] analyzed a quasi-experiment where eBay shut all the paid search advertising on Bing during a dispute with Microsoft but lost little traffic. These quasi-experimental results inspired a follow-up field experiment where eBay randomized suspension of its branded paid search advertising and found results consistent with the quasi-experiment. Reflecting the importance of such methods at firms, some companies provide causal inference training for their data scientists ([32]; [81]). The ability to make causal claims is highly valuable in academia and in practice. This article aims to help both marketing scholars and practitioners conduct and evaluate the credibility of quasi-experimental studies.Quasi-experimental research, as in much work in applied statistics, begins with the equation y=f(X,ε;β) . The focus is then on whether a change in a single covariate x in the vector of X can be demonstrated to cause a change in y . This focus often enables the exploration of foundational questions in marketing, because marketers often have data representing the actions of many individual consumers or clients and need to understand the causal relationship between a particular x and y to make decisions about whether and how much x to use.A marketing article that successfully uses the quasi-experimental econometric approach considers the following nine topics, which are echoed in the structure of this article: Research Question: Do We Care Whether x Causes y? Data Question: How Can Researchers Find Data with Quasi-Experimental Variation in x? Identification Strategy: Does x Cause y to Change? Empirical Analysis: How Can Researchers Estimate the Effect of x on y? Challenges to Research Design: What if Variation in x Is Not Exogenous? Robustness: How Robust Is the Effect of x on y? Mechanism: Why Does x Cause y to Change? External Validity: How Generalizable Is the Effect of x on y? Apologies: What Remains Unproven and What Are the Caveats?We start by explaining why quasi-experimental scholars may appear obsessed with identification, and how this influences the choice of research question and data setting. Quasi-experiments come in different shades, ranging from an almost completely random exogenous shock to where the treatment assignment is only partly random. We suggest different frameworks to accommodate various levels of evidence depending on the strength of the underlying identification argument. We then turn to the importance of understanding the underlying mechanism behind the causal result. Typically, this means showing that the effect is largest where theory would predict and is smallest where theory would predict a negligible effect. We also emphasize that researchers need to be clear about the external validity of their study and apologize for what remains unconvincing. Why the Focus on Identification?Why are quasi-experimental scholars seemingly obsessed with identification? Identification is defined by the challenge that ""many different theoretical models and hence many different causal interpretations may be consistent with the same data"" ([58], p. 47). However, effective decision making requires an understanding whether a measured relationship is indeed causal.One way to describe this issue is through the ""potential outcomes approach"" developed by Jerzy Neyman, Donald Rubin, and others ([86]).[ 5] This approach starts with the insight that for any discrete treatment—which could be an event or explicit policy ( D )—each individual i has two possible outcomes: yi1 if the individual i experiences the treatment Di=1 , and yi0 if the individual i does not experience the treatment Di=0 The difference between the two is the causal effect. The identification problem occurs because a single individual i cannot both receive the treatment and not receive the treatment at the same time. Therefore, only one outcome is observed for each individual at any point in time. The unobserved outcome is called the ""counterfactual."" The unobservability of the counterfactual means that assumptions are required. The identification problem means that those who experience D, and those who do not, are different in unobserved ways.Random assignment solves the inference problem, as the ""unobserved ways"" should not matter ex ante ([31]). [88], p. 13) explain that ""if implemented correctly, random assignment creates two or more groups of units that are probabilistically similar to each other on the average."" With enough people assigned randomly to one group or another, the only meaningful difference between the groups will be a result of the treatment.Therefore, random assignment is often called the gold standard of identification ([70], p. 8). [12], p. 11) emphasize that ""the most credible and influential research designs use random assignment."" That said, we should be clear that field experiments are merely a gold standard for being able to plausibly claim causality, not the gold standard for empirical work ([34]). Indeed, in many marketing situations, experiments are not feasible, appropriate, or affordable ([44]).Quasi-experimental work, by contrast, is aimed to identify exogenous shocks or events that can approximate random assignment. Given that assignment is not random, a researcher's goal is to make the unobserved ways in which the treatment and control groups differ as untroubling as possible to the researcher and the reader and thereby mimic random assignment as closely as possible. Research Question: Do We Care Whether x Causes y?The first and hardest stage in this process is identifying a question in which marketing scholars, managers, or policy makers actually care whether x causes y. This is difficult because many of the y s and x s for which we can measure a causal relationship are (unfortunately) uninteresting. Therefore, researchers who do quasi-experimental research do best if they start not with the data or an exogenous shock but instead start by asking themselves, ""Suppose I convincingly showed that an increase in x increases y —who would care about this substantive issue?This means that the first stage requires the identification of a causal relationship that would be of interest to marketers or policy makers because their decisions will be usefully informed by a clear understanding of the consequences of a particular action. As marketing technology and practices change, the number of measurable, interesting, and unanswered questions grows. A variety of editorials in this journal and elsewhere focus on how researchers can identify important issues. For example, the January 2021 special issue of the Journal of Marketing was dedicated to finding important marketing research questions, as highlighted in the editorial ([36]. Other editorials that discuss ways to identify important research questions are [59]) and [26] in the Journal of Marketing, [87]) in the Journal of Consumer Research, [96] in Marketing Science, and [50]) in the Journal of Marketing Research. Data Question: How Can Researchers Find Data with Quasi-Experimental Variation in x?[12], p. 7) explain that an identification strategy ""describe[s] the manner in which a researcher uses observational data or data that is not generated as part of an intentional experiment, to approximate a real experiment."" They suggest first thinking of an ideal randomized experiment that can address the research question. This helps the researcher see clearly why an effect may not be identified causally in a nonexperimental setting.As [75], p. 151) discusses, ""Good natural experiments are studies in which there is a transparent exogenous source of variation in the explanatory variables that determine treatment assignment."" Unfortunately, there is no universally accepted interpretation of what it means to have a transparent exogenous source of variation. Therefore, [75] (p. 151) emphasizes the importance of clarifying identification assumptions and understanding the institutional setting, stating, ""If one cannot experimentally control the variation one is using, one should understand its source."" In the marketing context, [84] discusses the dangers of using methods in which the source of the exogenous variation is either poorly understood or only weakly related to the correlation of interest.Much of the work using quasi-experimental variation in marketing settings uses mundane but easily understood events such as contract changes, regulation, individual-level life changes, or shifts in firm policy that did not occur because of an anticipated effect on the outcome of interest. In some sense, some of the best sources of exogenous variation are mundane: nonmundane sources of variation such as global pandemics or earthquakes tend to be associated with other things happening that make it difficult to establish a clean causal relationship.Table 1 lists several example quasi-experimental papers published in 2018, 2019, and 2020 in the Journal of Marketing, the Journal of Marketing Research, and Marketing Science. This table also summarizes the source of variation these articles use, spanning contractual changes; ecological variation (e.g., weather); geography; and macroeconomic, individual, organizational, and regulatory changes. It is useful to consider in turn why each of these sources of variation can approximate random assignment.GraphTable 1. Examples of Quasi-Experiment Studies in Journal of Marketing, Journal of Marketing Research, and Marketing Science in 2018–2020. 1 Notes: DMA = designated market area. ContractualTo find plausibly exogenous variation in timing, it often depends on an argument that the exact timing of a measure is plausibly exogenous. [28] argued that the timing of a dispute between the Associated Press and Google was essentially random as it was influenced by a contract negotiated many years previously, and so the timing could be used to study the effect of the removal of content from news aggregators on downstream news websites. EcologicalGenerally, within-season variation in weather is plausibly exogenous. For example, [95] uses quasi-experimental variation in actual and expected pollen counts. Key to the identification strategy is the focus on deviations from what was expected by firms. GeographicalWork using geographical boundaries often exploits the fact that people who live on either side of a demarcated geographic border are similar enough to be thought of as being randomized across them. For example, by looking at a remote border of Maryland that was geographically isolated from the rest of the state, [ 8] were able to argue that the imposition of sales tax for those who lived on one side of the border was random, relative to those people who lived nearby but just happened to be over the state border. MacroeconomicIt is also possible to take leverage of macroeconomic shocks. For example, [40] use the Great Recession as a key source of the variation on household incomes over time. They exploit the within-household variation in private label shares associated with within-household changes in income and wealth. The identifying assumption is that, conditional on all other factors, including an overall trend, within-household changes in income and wealth are as good as randomly assigned or exogenous changes. IndividualPlausibly exogenous variation can also be argued to occur at the individual level. For example, [20] use consumer migration to new locations as a quasi-experiment to study the causal impact of past experiences on current purchases. They argue that while migration is not necessarily random, the precise direction of migration can be, at least with respect to local brand market shares. OrganizationalShifts in firm policy and organizational events can also be leveraged as a source of variation. For example, [64] assess the change in customer behaviors between those whose information is breached and those whose information is not. The identification assumption is that the assignment of customers into the data breach group is likely to be random. RegulatoryMany papers also use the timing of regulatory changes as a source of variation. The argument here is typically that though the imposition of regulation may not be random, the timing of the regulation is. For example, [97] use a change in Massachusetts regulation of home sale listings to identify the effect of information about time on the market on house prices, and [76] use a change in the standardized nutrition labels on food products required by the Nutrition Labeling and Education Act and investigate how the Act changes brand nutritional quality.This discussion emphasizes that there are many potential sources of exogenous variation that can approximate a randomized experiment. We emphasize that typically the best papers focus on the research question first, and then imagine what the idealized experiment would look like to identify an actual quasi-experiment. Identification Strategy: Does x Really Cause y to Change?To convince a reader that an identification strategy is valid requires two steps. First, the researcher must explain where the variation they are calling exogenous comes from. This requires institutional knowledge and careful research into the setting. Second, the researcher needs to demonstrate that the relationship between the variation and the outcome of interest is very likely driven by the relationship between x and y and not by some other factor.To achieve the second requirement, it is useful to think about defending the experiment in terms of the exclusion restriction. Although the term ""exclusion restriction"" is often used specifically for instrumental variables, it is also a useful concept for other quasi-experiments. The exclusion restriction states that the quasi-experiment only affects y because it affects x.There are a variety of ways in which the exclusion restriction can fail, and so researchers look for exogenous variation in x that will have no direct effect on y. For example, [91] use wind speed as a quasi-experiment to provide an exogenous driver of posting to a user-generated content site about windsurfing. This allows them to understand the relationship between content creation and the creation of social ties. The argument for the exclusion restriction is that there is no other plausible way that wind could affect the creation of social ties except through content creation. As they mention in the paper, plausible challenges to this exclusion restriction are that windy days could affect friendship formation directly because users meet future online friends at windier surf locations. To address such challenges, the researchers present empirical data to suggest that the social ties that are being formed do not seem to reflect geography.Another example is [66], which examines the effect of delays in the early part of a banking technology adoption process on ultimate usage. Through a quasi-experiment that provides a source of exogenous variation in delays, they exploit the fact that Germany has a highly regulated system of public holidays and vacations that vary at the state level to prevent freeways from becoming overly congested. This leads to delays in technology adoption in that particular period to customers in one state, and not in others. The exclusion restriction is that there is no other reason that vacations or public holidays in the few days surrounding adoption would affect ultimate usage except through delaying the ability to navigate the security protocols required to sign up for the online banking service. One challenge for the exclusion restriction could be that individuals who sign up for a banking service around public holidays are somehow systematically different from others in terms of their laziness or motivation. To counter this challenge, the researchers present evidence that users are not different along any observable dimension.The exclusion restriction can also fail because of spillovers between groups that receive the exogenous shock or treatment and those that do not. The assumption that treatment of unit i affects only the outcome of unit i is called the stable unit treatment value assumption (SUTVA) in the treatment literature ([10]; [61]). This is not a trivial assumption. For example, [ 5] use the 2011 Orbitz–American Airlines disputes as an exogeneous event that led to a five-month period in which American fares were not displayed on Orbitz. The authors use this dispute to identify which company was hurt the most in terms of site visits and purchases. The SUTVA requires a valid control group such that the Orbitz–American Airlines disputes have no spillover on that group. As a result, the authors chose not to use airfare- or hotel-booking websites as a control due to the possible spillovers from Orbitz to other websites where customers can purchase. Instead, the authors used consumers' search of Lonely Planet as the control, because Lonely Planet is a travel website that is rarely used for bookings. The underlying idea is that an exclusion restriction cannot hold if the fact that one group was treated may also affect the control group's behavior. The SUTVA is therefore part of an argument that researchers make about an appropriate exclusion restriction.Importantly, there is no formula for a convincing explanation and defense of the empirical identification strategy in quasi-experiments. Except in cases of random assignment, it is not possible to prove that the identifying assumption is right. Instead, the objective for the authors is to pursue projects only when they can convince themselves (and their readers) that the causal interpretation is more plausible than other possible explanations. It is impossible to prove the validity of a quasi-experiment, such as whether one set of U.S. states serves as a legitimate control group for another or whether the exclusion restriction holds in instrumental variables. The credibility of any quasi-experimental work therefore relies on the plausibility of the argument for causality rather than on any formal statistical test. Empirical Analysis: How Can Researchers Estimate the Effect of x on y ?After establishing the identification assumption through the underlying framework of an exclusion restriction, the next step is to explore the data and conduct analysis that allows measurement of the effect of interest. This measured causal relationship is what has the potential to inform decision making. We discuss three different regression analysis frameworks using quasi-experiments: difference-in-differences (DID), regression discontinuity, and instrumental variables (IV). At the heart of all these strategies is a similar argument about the validity of the quasi-experiment.Table 2 outlines eight key steps in the three regression analysis frameworks. As pointed out by [54]) and others, the techniques are very similar in terms of the underlying econometric theory. However, though similar in the conceptual ideas, in terms of practical implementation, presentation, and how the researcher should best reassure their audience about the validity of the technique, there are some differences, which we expand on. The three frameworks differ in the first four implementation steps. We discuss the first four steps for each of the three regression analysis frameworks and highlight the issues in common across the three analysis frameworks in the last four steps. We also emphasize that many excellent papers do not implement each step, and this description is not intended to lead to unproductive dogmatism.GraphTable 2. Quasi-Experimental Regression Analysis Frameworks. All of these methods implicitly rely on throwing out variation in the data that is not exogenous. In other words, they involve losing power to support the exogeneity assumption. This means that quasi-experimental work cannot use the R-squared as a useful summary of the appropriateness of the model. [41]) provide some useful evidence. While R-squared or a comparison of log-likelihoods is very useful in many other contexts (e.g., forecasts), benchmarking quasi-experimental analyses against other methods by using the R-squared will be misleading. Difference-in-Differences AnalysisA standard DID analysis compares a treatment group and a (quasi-) control group before and after the time of the treatment. The ""treatment"" is not truly a random experiment but, rather, some ""shock."" Unlike a simple comparison (or single-difference) analysis, DID methods generate a baseline for comparison between the treatment and the control group. By highlighting the change in the treatment group relative to the control group, DID enables the researcher to control for many of the most obvious sources of heterogeneity across groups.[47] is an example of a DID paper. The authors examine the impact of privacy regulation on the effectiveness of online advertising. In late 2003 and early 2004, many European countries implemented new restrictions on how firms could collect and use online data. The paper uses data on the success of nearly 10,000 online display advertising campaigns in Europe, the United States, and elsewhere between 2001 and 2008. The authors compare the change in effectiveness of the ad campaigns inside and outside Europe. Therefore, the first difference is the change in the campaign effectiveness, and the second difference is the change in Europe relative to elsewhere. Compared with before the regulation, ad campaigns became 2.8% less effective in Europe after the regulation. In contrast, compared with before the European regulation, ad campaigns became.1% more effective outside of Europe after the European regulation was implemented. Identification of Difference-in-DifferencesThe first step is to clearly lay out the identifying assumptions. [47], p. 63) state that ""the identification is based on the assumption that coinciding with the enactment of privacy laws, there was no systematic change in advertising effectiveness independent of the law"" and that ""the European campaigns and the European respondents do not systematically change over time for reasons other than the regulations."" A substantial portion of the paper is devoted to providing empirical evidence regarding whether ( 1) European ad agencies invest less in their ad creatives relative to non-European ad agencies after the laws, ( 2) the demographic profile of the respondents is representative of the general population of internet users, and ( 3) there may have been a change in European consumer attitudes and responsiveness to online advertising separate from the Privacy Directive.The analysis of consumer attitudes and ad responsiveness is based on a concern about unobservables, specifically whether there are alternative explanations for the measured changes in the attitudes of survey participants toward online advertising that were separate but contemporaneous with the change in European privacy laws. To check for such unobserved heterogeneity, [47]) examine the behavior of Europeans on non-European websites that are not covered by the European Privacy Directive to see if a similar shift in behavior can be observed, and they find evidence that changes in behavior are connected with the websites covered by the law, rather than the people taking the survey. The identification exclusion criterion is further validated by a mirror image of the falsification test by looking at residents of non–European Union (EU) countries who visited EU websites. When residents of non-EU countries visit EU websites, the ads are less effective in the postperiod. In contrast, when residents of these non-EU countries visit non-EU websites, there is no change in effectiveness before and after the EU regulation. Therefore, the results appear to be driven by what happens at EU websites rather than by a difference in how Europeans behave relative to non-Europeans. Raw Data Exploration of Difference-in-DifferencesThe second step is to explore the raw data. Before applying the DID framework, it is important to explore the raw data to assess whether the quasi-experiment appeared to have an effect. For example, when a treatment occurs in the middle of a time series, many papers use a graph that shows that before the treatment occurred, the treatment and control groups were on a similar trend and had similar values; then, after the treatment occurred, the trajectory of the treatment group diverged from the control group.Researchers should also assess whether their quasi-experimental setting meets the parallel trend assumption while exploring their raw data. This involves demonstrating that behaviors were similar in the period prior to the policy change across the treatment and control groups. Depending on the length of the time period, this can be done by conducting two-sample mean comparisons for each pretreatment period or by running a linear regression and looking at the time trend differences between the control and treatment groups. It is also often ideal to simply plot the raw data to support this point.Though it is desirable and convincing if the main effect of interest can be seen through descriptive statistics or visualization, we caution that this is not always possible. This may happen because effect sizes are small—as they often are in advertising—or because there is variation in the data that is best addressed using a regression framework. Analysis of Difference-in-DifferencesAlthough a DID regression can be represented in a 2 × 2 table, it is usually analyzed with regression analysis to allow researchers to control for factors that may change over time and across individuals. The simplest version of this regression is as follows: yit=α1TreatmentGroupi+α2AfterTreatmentt+βTreatmentGroupi×AfterTreatmentt+γXit+εit, Graph( 1)where y is the outcome of interest; i represents the individual, firm, or other cross-sectional unit of interest; t represents the time period; and εit represents the error. The key focus of the DID specification is on β , which captures the explanatory power of the crucial interaction term. Usually, researchers add controls Xit to address additional omitted variables concerns, such as an observed covariate that may not affect the treatment and control groups in the same way.When researchers have access to a panel, it is possible to address this concern directly by observing the same individuals, or the same campaigns, both before and after the timing of the treatment. It is then possible to add fixed effects to control for all individual-level (time-invariant) heterogeneity. Furthermore, if the data set includes more than two time periods, then adding time-specific fixed effects controls for all time-period-specific heterogeneity (across all individuals). With individual and time fixed effects, the DID regression is yit=βTreatmentGroupi×AfterTreatmentt+γXit+μi+τt+εit, Graph( 2)where μi is the individual-level fixed effect and τt is the time-period fixed effect. The fixed effects mean that the main effect of TreatmentGroupi and AfterTreatmentt drop out because they are collinear with the fixed effects. If possible, it is often desirable to difference out, rather than estimate, the fixed effects to avoid bias due to the incidental parameters problem (e.g., [67]). Most standard statistical packages automatically condition out the individual fixed effects from fixed effects panel data models where possible.[ 6]Though changes over time are common, DID methods do not require a time-series component. For example, [48] examine the impact of offline advertising restrictions on prices for keyword advertisements. The first difference is the keyword ad prices in states that have restrictions compared with states that do not. The second difference is the keywords that are affected by the restrictions compared with the keywords that are not.For quasi-experimental analyses that do examine changes over time, another tweak is that quasi-experimental treatment can occur at different times, meaning that individuals are treated at different times and that the AfterTreatment variable can change with subscripts i and t. For example, [27] study how a book review posted on Amazon affects sales of that book on Amazon, compared with sales of that book at barnesandnoble.com. Different books are reviewed at different times. Therefore, the treatment here is the review a book receives, and the AfterTreatment period occurs at different times for different books. [14], [19], [21], [35]), [49], and [85] explore the effects of variation in treatment timing. The issue is that because a fixed-effects DID estimator is a weighted sum of the treatment effect in each group and at each period, even though the weights sum to one, negative weights may arise when there is a substantial amount of heterogeneity in the treatment effects over time. A related concern has been highlighted by [45], who emphasize the problems that occur when both the treatment effect and treatment variance vary across groups.This means that researchers should be cautious in summarizing time-varying treatment effects with a homogeneous treatment effect as in the two-way DID framework if there is a substantial timing dimension. To address these issues, researchers have proposed a variety of estimators that allow for a cleaner comparison between the treated group and the control group. Both [21] and [35]) propose new estimands to estimate treatment effects in the presence of heterogeneity across groups and over time.[ 7] Another approach is taken by [93], who discuss corrections that should be applicable in a situation where leads or lags might be expected.Overall, DID is a powerful tool for helping identify the causal relationships that managers need for effective decision making. It can enable researchers to control for time-invariant individual-level heterogeneity, relying on the assumption that differences in the changes that the treatment and control groups experience over time are driven by the impact of the treatment. Regression Discontinuity AnalysisRegression discontinuity is a quasi-experimental technique in which the ""experiment"" relies on an exogenous arbitrary threshold. As [60], p. 616) put it, ""The basic idea behind the RD [regression discontinuity] design is that assignment to the treatment is determined, either completely or partly, by the value of the predictor being on either side of a fixed threshold."" Identification in regression discontinuityRegression discontinuity may be particularly useful to marketing scholars. [56] argue that many marketing interventions are based on thresholds of real or expected consumer or firm behavior. For example, direct mail companies use the scoring policies for recency, frequency, monetary models. Consumers just above and just below the cutoff should be similar in many dimensions, and their outcomes can be compared to assess the impact of the different mailings.Similarly, government policies based on firm size can provide a useful identification strategy for marketing scholars. For example, requirements for firms to post calories, undertake layoffs, and provide benefits often depend on the number of employees or other measures of firm size. By comparing firms just above and just below the threshold, it is possible to assess the effect of the policies on firm behavior.A regression discontinuity design implies that treatment is assigned depending on whether a continuous score zi crosses a cutoff z¯ . The analysis then focuses on whether there is a change in the outcome of interest y in the neighborhood of z¯ ([56]). In general, if a threshold is used as the source of the quasi-experiment, particular attention should be devoted to the source of the threshold and providing evidence that the threshold is essentially arbitrary and not likely to be linked to underlying discontinuities in behavior. Any discontinuity in the effect is assumed to be due to the treatment.This assumption is not always innocuous. Consider a $50 cutoff for receiving a marketing incentive. If the firm promotes the threshold and consumers try to achieve it, then there might be a substantial difference between people who spend $49 and people who spend $51. Those who spend $49 are likely to be unresponsive to the incentive because they did not try to cross the threshold to get the incentive. In contrast, those with exactly $50 in spending might have selectively chosen to spend exactly enough to get an incentive that they planned to use. It is important to address the potential for such concerns directly.This is reflected in a debate in economics about the effect of thresholds for low birth weight on medical outcomes. In an initial study, [ 6] used the fact that birth weight threshold of 1.5 kg is used to determine whether the newborn receives intensive medical treatment. In a critique of this work, [15] show that the children placed just at the cutoff seem to have significantly worse outcomes than babies on either side of the cutoff. This is evidence against use of this discontinuity for identification. [15] state, ""This may be a signal that poor-quality hospitals have relatively high propensities to round birth weights but is also consistent with manipulation of recorded birth weights by doctors, nurses, or parents to obtain favorable treatment for their children"" (p. 2119). Raw data exploration of regression discontinuityOnce the researcher has found a regression discontinuity setting, the first step is to explore whether the discontinuity is arbitrary and linked to discontinuities in any other variables. For example, [59] examine the relationship between online reviews and advertising spending in the hotel industry. They exploit the regression discontinuity design of the rounding rule that TripAdvisor uses to convert the average ratings of reviewers into the nearest half or full star (i.e., a rating of 3.74 is shown as 3.5 stars while a rating of 3.75 shown as 4 stars), building on work by [71]. The key identification argument is that the rounding mechanism creates discrete, random variations in perceived quality around the rounding threshold and is independent of a hotel's true quality.A threat to the arbitrary discontinuity threshold would be that hotels manipulate their average ratings around the rounding thresholds. [59] argue that if there is upward manipulation of ratings, there would be relatively few firms with average ratings just below the thresholds and a clump of firms with average ratings just above the thresholds. They show instead that the density of average ratings is uniform, with neither bumps nor dips above or below the round thresholds. They provide additional empirical evidence that characteristics of the hotels do not differ systematically above or below the threshold. Neither do they observe discontinuities in other key variables such as hotel prices and the number of five-star reviews. Analysis of regression discontinuityThe equation used for regression discontinuity can be written for panel data as yit=βI(zit≥z¯)+γXit+μi+τt+εit. Graph( 3)Here β is the treatment effect, the parameter of interest. Xi represents covariates. I(zit≥z¯) is an indicator function that equals one when zit≥z¯ and zero otherwise. One final consideration is how to select the appropriate bandwidth for a regression discontinuity design, which is the question of how one decides on the sample to analyze, in terms of how far away the people in the sample are from the threshold where the discontinuity occurs. In general, such decisions have often been rather ad hoc, but there is an emerging literature that can help guide the researcher into thinking about how to take a more conservative approach to selecting bandwidth given the data at hand ([25]). The researcher should also ensure that their results are not sensitive to the choice of bandwidth. As with other quasi-experimental methods, the validity of the method cannot be statistically proven. Therefore, substantial emphasis must be placed on the explanation and defense of the quasi-experiment using raw data. Instrumental Variables AnalysisThe quasi-experimental perspective on IVs is somewhat different from the standard treatment in econometrics textbooks, which focuses on simultaneous equations and a more structural approach. The differences relate to justification and interpretation. The quasi-experimental approach emphasizes that the shocks that move the instrument should behave as if they are an experiment. The quasi-experimental approach gives a sense of the sign, significance, and magnitude of the causal effects. The structural approach emphasizes that the shocks should be motivated by an economic model that explains the exclusion restriction. The IV approach used in structural models gives elasticities that can be used to generate counterfactuals outside of the sample. Despite these differences in interpretation, it is important to remember that the underlying mathematics is identical. Identification of instrumental variablesThe basic idea behind using IVs is that the covariate of interest x contains both useful variation (to identify the causal effect of interest) and less useful variation (that confounds the effect). A good instrument z is strongly correlated with the useful variation but uncorrelated with the confounding variation. In other words, the researcher only uses the variation in x that can be explained by the exogenous shifter z.The standard two-stage model involves two steps. In the first-stage regression, a fitted value of xi^ can be obtained by regressing x on instrument z and covariates W : xi=γzi+ϑWi+ηi. Graph( 4)In the second-stage regression, the IV estimator β^ is obtained by regressing the outcome y on the fitted value of x^ and covariates W : yi=βxi^+φWi+εi. Graph( 5)The identification of the effect of x on y relies on the following ""reduced form."" Inserting the predicted x to the y equation will give Equation 6. Here, φ^ is used to highlight that when regressing y directly on instrument z and covariates W, the estimated covariate coefficient is rescaled as φ^=βϑ+φ . yi=βγzi+φ^Wi+εi. Graph( 6)Therefore, from the quasi-experimental point of view, an instrumental variable can be seen as a treatment that affects the endogenous covariate directly. This means that directly regressing the outcome of interest on the instrument (in one stage) will get the causal effect of interest, but it will not be properly scaled. The purpose of implementing two stages is to scale the treatment effect properly. There are many ways of operationalizing instrumental variables, and this can be a place for highly technical tools. We emphasize the simplest two-stage least squares (2SLS) approach, but the intuition behind the role of instrumental variables as an identification strategy remains regardless of functional form assumptions. Using two stages enables the researcher to disentangle β and γ . In other words, two stages are needed to get the elasticity right, but the experiment happens at the level of the instrument and so, even though the focus is on the relationship between x and y, the intuition on causality happens at the level of the relationship between z and y.Returning to [91], while the paper adds some additional necessary nuance to the estimation to fit the particular situation, the intuition on causality measures the impact of wind (the instrument z ) on social ties (the outcome of interest y ). This will be βγ . The relationship of interest, however, is the impact of posts ( x ) on social ties ( y ), which is measured as β .IV can be a less transparent solution to identifying causal effects compared with the other two analysis frameworks discussed previously (for a detailed discussion, see [84]). The distinction between the relationship of interest ( β ) and the direct estimate from the quasi-experiment ( βγ ) means that it is sometimes harder to visualize how the quasi-experimental variation works in IVs.Transparent communication of IV analysis is difficult for three reasons. First, in contrast to the binary nature of the exogenous variation in DID and regression discontinuity, instruments are often continuous. This makes it more difficult to communicate the intuition for why the variation is exogenous to the potential for omitted variables or simultaneity. The ability to use continuous instruments (and multiple instruments) can also be seen as a strength of IV techniques. They enable a more flexible set of counterfactuals because there are more treatments observed and used in the analysis. For example, while a discrete quasi-experiment on retailer discounts would allow the researcher to compare the impact of a small set of retailer discounts on sales, a continuous instrument for the discounts might allow the researcher to compare a variety of smaller and larger discounts.Second, weak instruments are a challenge. Instrumental variables techniques are consistent but biased, and this bias can matter even in seemingly large samples ([92]). Weak instruments can lead to incorrect inference in which the bias of the weak instrument dominates the potential bias of the omitted variables.Knowing the context and the institutional setting can be invaluable in identifying strong IVs. For example, [76] derived their instruments for brand taste and price from the authors' intimate knowledge of the regulation and food industry. There are also recent advances in econometric methods that allow for more accurate presentation of statistical significance when instruments are weak ([68]). As [11] point out, many of the challenges of weak instruments are magnified when authors use multiple instruments to deal with multiple sources of endogeneity. By contrast, a focus on a single endogenous variable with a single source of endogenous variation has attractive statistical properties as well as being more transparent to the reader.Third, many researchers present IV results with different tests and with different norms. This makes it difficult to read and assess the validity of papers with instruments. Raw data exploration and analysis of Instrumental Variables[12], pp. 212–13) provide a sequence of steps to follow in an attempt to standardize practice. In presenting this list, we hope that it does not lead to unproductive dogmatism, and we emphasize that this is just one possible way to communicate the rationale behind a causal interpretation of the results. Still, we hope that in following these steps to the extent possible, marketing scholars can avoid being subject to many of the criticisms highlighted by [84]. The steps are as follows: Regress the outcome directly on the instrument. When using IV techniques, it is also desirable to show the reduced form result of regressing the outcome directly on the instrument. Because this is an ordinary least squares regression, it is unbiased. At the very least, the researcher should be confident that the instrument ( z ) has the expected direct effect on the outcome ( y ). Report the first stage. Assess whether the signs and magnitudes of the coefficients make sense. Report the F-statistic on the excluded instruments. This helps determine whether the instruments are weak. [92] advise that F-statistics below 10 in case of only one instrument suggest weak instruments, though, as [12], p. 213) note, ""Obviously this cannot be a theorem."" Similarly, [84] suggests reporting the first stage with and without the instruments to document the incremental impact of the instruments on the R-squared. If there are multiple instruments, report the first- and second-stage results for each instrument separately (at least in the appendix) because bias is less likely if there is only one instrument. Presenting the results separately also helps the reader understand the intuition behind the quasi-experiment underlying each instrument—whether the multiple instruments use different variation in increasing the exogenous shift in x. If there are multiple instruments, an overidentification test such as the Sargan–Hansen J can be performed to test whether all instruments are uncorrelated with the 2SLS residuals.[ 8] However, given the difficulty of identifying a robust instrument, it is unusual for researchers to have convincing cases for multiple instruments in a way that leads their regression to be overidentified. In other words, increasingly, standard practice is to focus on one instrument rather than many ([11]). Conduct a Hausman test comparing ordinary least squares and instrumental variables. If the results change, reflect on whether they change in a direction that makes sense given the power of the instrument. Do not interpret the results of the Hausman test to prove that the endogeneity problem is irrelevant. As noted by [84], the instrument may not be valid and therefore the test would be uninformative. Assess whether there is a weak instrument problem. For example, in a linear model, compare the 2SLS results with the limited information maximum likelihood results. When there is a weak instrument, the two-stage least square estimators are biased in small sample. Limited information maximum likelihood estimators have better small sample properties than 2SLS with weak instruments. If the two estimates are different, there may be a weak instrument problem. Any inconsistency from a small violation of the exclusion restriction gets magnified by weak instruments. Presentation of Results and Clustering of ErrorsRegardless of which regression analysis framework to employ, presentation of baseline estimates and standard errors, along with a set of robustness checks ([59]) is standard. This typically appears in the form of a regression table with several different specifications. For example, the first column might not include any controls beyond the fixed effects, and the next set of columns might add controls. The economic magnitude of the coefficients should be discussed, both with respect to changes in the covariate of interest and relative to the range and standard deviation of the covariate and dependent variable.A key issue in quasi-experimental analysis is correlated errors in observations, because the outcome is often observed at a finer level than the treatment. For example, the researcher might observe treatment and control groups for several advertising campaigns over a long time period. For each campaign, the researcher might have data on many individuals per campaign and many time periods per individual; however, the choices of the same individual in many time periods are likely to be correlated. [16] emphasized that failure to control for the correlation between these choices will lead to an overstatement of the effective degrees of freedom in the data, and therefore, standard errors will be biased downward. They suggest clustering standard errors by individual over time to address this issue and provide Monte Carlo evidence that clustering is likely to lead to robust inference.Similarly, [38] emphasize that if individual responses to the same treatment are likely to be correlated, for example, because of close physical or social proximity, clustering standard errors by groups of individuals is a conservative and useful way to estimate standard errors. Researchers often need to decide on the size of the clusters. For example, in studying ready-to-eat breakfast cereals, is the correct unit the company such as General Mills, the brand such as Cheerios, or the sub-brand such as Honey Nut Cheerios? The answer depends on the data and research question. If the data are at a lower unit level (e.g., individuals) than a treatment that takes place at the firm level, cluster the standard errors at the level of the treatment. A useful perspective on this is provided by [ 2], who remind researchers that the major driver for clustering should be the experimental design rather than simple expectations of correlation. More recently, there has been evidence suggesting that it is undesirable to cluster on the variable that determines whether that observation is subject to the regression discontinuity design (e.g., age). The answer is often instead simply to reduce the bandwidth across which the regression discontinuity is studied ([65]).Clustered standard errors rely on consistency arguments and large samples. With a small number of clusters, alternative methods are needed, such as those developed by [22], [30], and [53]. For example, [43] investigate consumers' dynamic responses to price promotions in a retail setting that involved randomly assigning ten supermarkets into varying promotion depths. Given that treatment takes place at the store level while the observation is at the consumer level, each consumer's effective contribution to reducing standard error estimates is likely to be lower than in a setting where there is no correlation across observations. However, given the relatively small number of stores/clusters available in this setting, the authors implement the wild bootstrap procedure, as proposed by [22], to correct for downward bias potentially induced in small samples. However, [24] show that even this approach requires rather large assumptions. Challenges to Research Design: What if Variation in x is not Exogenous?A more general point is that quasi-experiments range in how plausible the exogenous variation underlying the paper is, ranging from cases where the allocation is almost completely random to less clear cases where a firm or consumer assignment to treatment or control is partly random and partly an endogenous choice. Perhaps the ideal thought experiment here is [101], whose treatment and control were a pair of kidneys from the same person. [101] finds that in the United States, even identical kidneys from the same donor are received differently depending on the observed number of rejections preceding the recipient in the queue. Most research settings are less favorable. In such settings, it is often useful to combine different approaches in the same paper. For example, [79] combines a DID strategy with counterfeit entry as the treatment with a convincing and high-powered instrument on government regulation.Still, there will be situations where a compelling exclusion restriction is lacking or the treatment–control allocation appears far from random. If the treatment and control groups are substantially different in the pretreatment or if the treatment appears to be applied based on selected characteristics, the control group is unlikely to be a good proxy for the counterfactual, and the quasi-experiment may be less likely to be valid.We provide a discussion of three methods that are further steps researchers can take when comparability between the control and treatment groups is violated. They vary in terms of the observed and potentially unobserved differences between the control and treatment groups. Table 3 provides a summary of the frameworks and when to apply them. The table emphasizes that researchers should be cautious about applying matching methods or correction for selection bias on the grounds that there are no plausible exclusion restrictions, because these methods still require the researcher to make an argument about an exclusion restriction. The technical details of matching methods or selection bias correction are different from the three methods described previously, but the idea is similar in nature. The main goal is to bring in additional data to create control and treatment groups that are like those in quasi-experiment studies.GraphTable 3. Steps if Researchers Are Worried They Do Not Meet the Exclusion Restriction. Propensity Score MatchingMatching methods, pioneered by [83], have been developed such that the outcomes of the treated are contrasted only against the outcomes of comparable untreated units. Many published articles in marketing have used propensity score matching when comparability between the control and treatment groups is violated. An assumption of propensity score matching is that there are observable control variables capable of identifying the selection into treatment and control conditions. This is not a trivial assumption. It suggests that propensity score matching is only good if the exclusion restriction is met conditional on the variables in the match. Any matching procedure to make the control and treatment more similar in the observables can be seen as a flexible functional form with adding ""control variables"" to an analysis framework. Propensity score matching requires subject-matter knowledge regarding the role of covariates in the treatment assignment decision and whether the exclusion restriction is satisfied conditional on the covariates. Therefore, we caution against applying matching methods without convincing justification of exclusion restriction.It is difficult to identify a standard procedure for propensity score matching. We refer to [61] as a good starting point. The general objective of propensity score matching is to estimate a score such that the distribution of all the observed variables and behaviors among the treated units is similar to that among the control units. In this discussion, we consider the set of treated units to be fixed a priori. Four steps are involved in the propensity-score-matching procedure.First, choose a functional form of the propensity score. The basic strategy uses logistic regression to model the probability of receiving the treatment given a set of observables. Second, measure the distance and apply a matching algorithm. Several possible matching methods are available including, for example, nearest-neighbor matching based on the distance in the estimated propensity score or multiple matching using all controls within some distance from the treated unit. Third, assess the degree of overlap in the distribution of the linearized propensity score after matching. Researchers typically plot and compare the histogram-based estimate of the distribution of the linearized propensity score (logarithm odds ratio) for the treatment and control groups. To inspect the match quality, it is useful to show tables on the distribution of the estimated propensity scores and the mean values of some key variables for the treated and untreated over different propensity score intervals.[ 9] Fourth and finally, calculate the average treatment effect (ATE) with the matched sample using, for example, the DID regression analysis framework discussed previously.There are at least two caveats regarding propensity score matching. First, the model for the propensity score may be misspecified. In that case, the balance in covariates conditional on the estimated propensity score may not hold, and the credibility of subsequent inferences may be compromised. This calls for a careful discussion on the role of covariates in the treatment assignment decision. Specifically, it is important to provide a discussion of whether the covariates can be considered exogenous to the treatment. Second, regardless of the number of observed covariates used, propensity score matching does not account for the potential selection on unobservables in treatment assignment. It is important to explain why controlling for observables will address concerns with the exclusion restriction or why unobservables are not an issue in treatment assignment. Synthetic Control MethodsIn some cases, even the closest match may not be close enough. This is particularly relevant when researchers are interested in how an event, regulatory intervention, or firm policy change affects the evolution of the outcome of interest, in contexts where only a modest number of treated units (possibly only a single one) and control units are observed for a large number of periods before and after the event. Two aspects make this setting different from the typical use of the propensity-score-matching method. First, matching is done over the pretreatment outcomes in each period rather than a number of covariates. Second, the number of control units and the number of pretreatment periods can be of similar magnitude. Synthetic controls use a different convex combination of the available control units ([ 3]; [ 4]; [39]). The intuition behind this method is that the created synthetic control unit closely represents the treated unit in all the pretreatment periods and affords time-varying causal inference on the trajectory of the outcome of interest.Synthetic control has been used in multiple recent studies with quasi-experimental design ([ 1]). For example, [51] analyze the causal effect of industry payment disclosure on physician prescription behavior, [99] assess the impact of mobile hailing technology adoption on drivers' hourly earnings, and [78] study the causal effect of online paywalls on the sales revenues of newspapers.Like propensity score matching, synthetic control methods are statistically rich, but they do not replace a carefully thought-out exclusion restriction and identification argument. Put differently, if propensity scores or synthetic controls appear to work when the treatment and control group are not similar, it is important to explain why controlling for observables will address issues with the exclusion restriction. In many cases, such explanations are weak and the exclusion restriction is unlikely to hold. Recent work in economics emphasizes this by showing the benefits of combining a synthetic control method with a strong exclusion restriction ([13]). Selection Bias Correction MethodMany papers written in marketing involve a comparison of potentially different groups that reflect endogenous choices by companies or consumers where the allocation to the treatment condition is not fully random. For example, [46] assess if the introduction of the free mobile app in a business-to-business context increases sales revenues from buyers who adopted the app. In an ideal setting, the company could randomize the treatment, then observe sales from buyers who did not get the app and sales from buyers who did get it. However, this company's app was available to all buyers. Therefore, the buyers' app adoption is not random, and self-selection into the treatment (adoption) group needs to be addressed. Omitted variables that drive strategic app adoption could correlate with the sales from these buyers.When this happens, it is sometimes useful to estimate a Heckman selection model ([57]), which explicitly models selection into the treatment as a two-step process. As [100], p. 564) pointed out, the exclusion criterion is still key to the identification of the treatment effect of interest in the two-step estimation procedure. Without the exclusion criterion, the effect of the treatment is identified only due to the nonlinearity in the functional form (specifically through the inverse Mills ratio). This may lead to severe collinearity and imprecision in the standard errors. More importantly, without a strong and credible exclusion restriction, identification in this setting is driven by the assumed functional form.In other words, although the Heckman correction will provide an estimate without an exclusion restriction, that estimate depends entirely on the assumption that the error structure is bivariate normal. When there is an argument for the exclusion restriction, a selection model is helpful. In the absence of the exclusion restriction, even if combined with other techniques such as propensity score matching, the results would be identified off the functional form assumption alone. Put differently, if one of the covariates in the correction equation satisfies the exclusion restriction, then it is the variation in that variable that identifies the control for selection. In contrast, if the covariates in the first step are all also in the second step, then it is only the assumed error structure that identifies the control for selection.There are both similarities and differences between selection bias correction and instrumental variable approaches. There are also similarities with the control function approach in terms of the importance of functional form assumptions on the errors in the absence of an exclusion restriction. Control functions are not part of the standard quasi-experimental toolkit, so we do not provide a detailed discussion. The selection bias correction approach uses the instrument to control for the effect of unobservables, while the instrumental variable approach attempts to eliminate the threat of endogeneity by only leveraging the useful variation created by the instrument. Yet, the two approaches share the basic idea of using an exclusion criterion (or instrument). Ultimately, both rely on the ability to find an exclusion restriction that creates useful and exogenous variation. This is why we emphasize the importance of identification in quasi-experiments and caution against blindly applying a correction for selection bias without carefully thinking about the identification assumption and providing a justification for why the exclusion restriction holds. Selection bias correction approaches are therefore only useful for causal inference in the presence of a strong credible exclusion restriction. Robustness: How Robust is the Effect of x on y ?The specific robustness checks chosen will depend on the exact context. With electronic appendices and increasingly cheap computation, it is possible to show robustness to a large number of alternative specifications. Here, empirical work with quasi-experimental methods differs substantially from research using forecasting models. The aim is not to show one specification (or model) and defend it. Instead, the idea is to show that the sign, significance, and magnitude of the estimate of β remain broadly consistent across a vast range of possible models ([59]). Often these robustness checks are dropped from the published version of the article, though they are very useful in the referee process and can end up as part of an online appendix. The following subsections describe some examples of useful robustness checks. Different ControlsCompare the coefficient of interest in the models with and without controls. For example, if the coefficient changes from 2.5 to 3.5, then this change (+1.0 in this example) is informative about how big the impact of the omitted variables has to be relative to the observed controls for the omitted variables to drive the result. [ 7] provide a method to examine how much the effect of interest changes as controls are added, and then to assess how important the omitted variables would have to be for the treatment effect to disappear. The method is based on Rosenbaum bounds ([37]; [82]). It has been applied in the marketing literature by [73] and extended by [90]. Although the formal method is useful, as discussed in [77], many researchers ([ 9]; [74]) use the more basic insight that there is information in the impact of the controls on the measured effect of interest. This does not mean that results are invalid if the controls do change the estimated effect substantially, but documenting that adding seemingly relevant controls does not change the results can provide further support for the causal interpretation. Different Functional FormsResults should not depend on arbitrary choices of functional form. For example, if using a linear probability model, show robustness to logit and probit. The choice between linear probability models and nonlinear models such as logit is widely debated. [12] argue for linear probability models because they are simple to interpret and consistent under a basic set of assumptions. Others argue against them because they are inefficient (and inconsistent if the assumptions are violated). In cases like this, where the literature does not give clear guidance on the choice of model, showing robustness to different choices is optimal. Different Choices of the Time Period Under StudyResearchers often can choose when to start and end the sample. For example, for a treatment that occurs in 2004, researchers should be comfortable that the results are robust to the arbitrary choice of whether the period studied is 2002 to 2006, 2000 to 2008, 1995 to 2015, and so on. Different Dependent VariablesThere might be several different dependent variables that relate to the outcome of interest. Showing robustness to these related outcomes increases confidence in results. Different Choices of the Size of the Control GroupResearchers choose whether all the data should be used in the control group, or only a subset of the data that is ""close"" to the treatment group (e.g., as measured by a propensity score). Researchers can also choose how to define the treatment group. Placebo TestsThe idea of a placebo test is to repeat your analysis using a different part of the data set where no intervention occurred. For example, if the quasi-experimental shock happens this year, instead of comparing the difference in the outcome between last year and this year between the control and treatment groups, you can conduct a placebo test by redoing the analysis and compare the difference in the outcome between the control and treatment groups using periods with no intervention shocks. Alternatively, analysis can be conducted on an outcome that should be unrelated to the intervention being studied. The goal is to establish a null effect when there is not supposed to be one.It is unlikely that every robustness check will yield the same level of significance or the same-sized point estimate as the initial specification. Researchers (and reviewers) should therefore not expect every specification to yield the exact same results. The key is to communicate when the results hold up. This will consequently help inform the reader what drives the statistical power behind the results.Broadly, quasi-experimental research aspires to identify effects that do not rely on the underlying assumptions outside of the experimental variation. There are many places where that can break down, including functional form assumptions, external validity, and various confounding effects. The focus is on a robust single causal relationship. Mechanism: Why Does x Cause y to Change?The most effective papers typically do not stop with identifying a causal effect and its magnitude. After identifying a likely causal relationship, it is important to assess why x causes y to shift. Understanding mechanisms is often a key goal of social science. There are at least three benefits of establishing mechanisms. First, it provides a rationale for why the effect should exist in the first place. It requires the authors to think about the theoretical contribution of their research more carefully and helps make the argument for causal identification more convincing. Second, identifying mechanisms can help evaluate the benefits and negative consequences of the intervention and identify avenues for course correction, if needed. Third, understanding mechanisms allows for the possibility to extrapolate the findings to other contexts. Research needs to provide guidance on when and why the causal relationship is relevant. Assessing the Mechanism Through Mediation AnalysisWhen the data afford a direct measure of mediator variables, mechanisms can be inferred by mediator analysis. To illustrate how quasi-experiments can show process through mediation, we use [52] as an example. They investigate whether a variable compensation scheme increases salespeople's stress, resulting in emotional exhaustion and more sick days, and counteracts the sales benefits companies might expect from variable compensation schemes. In one of their empirical analyses, they use a natural experiment where a company dropped the variable compensation share from 80% to 20% in one of its business units. To test the health state as a possible mediator variable, they were able to measure sick days both before and after the change in the variable compensation share. In the country of study, sick days are strictly regulated by law and require certification by a physician (at the latest on the third day of the leave). Those who take more than three sick days in a given month are more likely to have substantial health problems. They measure the sick days counting after the third sick day in a month.Combining the DID analysis with mediator analysis, [52] show that the direct effect of the treatment (drop in variable compensation share) on sales performance is significant and negative, and that the indirect effect of the treatment on sales performance via sick days is positive and significant. The mediator analysis suggests that a higher variable compensation share is associated with enhanced sales performance but also with more sick days, which, in turn, reduce the gains to sales performance. Assessing the Mechanism Through Moderation AnalysisHeterogeneous treatment effects can be used to test behavioral mechanisms. In a quasi-experimental setting, mechanism checks via heterogeneous treatment effects, sometimes referred to as falsification checks, are not simply equal to identifying moderators. They involve identifying which groups would be affected by a certain mechanism that would display the causal effect of interest, and which other groups would not display the causal effect of interest by the proposed mechanism.Moderation analysis therefore serves a broader purpose by providing an opportunity to help explore the behavioral mechanism. If the effect goes away when theory suggests it should, then this helps identify why it happens. If the effect is larger when theory suggests it should be, then this also helps identify the mechanism. A simple approach is to estimate the effect separately by whether an individual is a member of a group that theory suggests should experience a bigger effect. Formal testing of whether the difference is statistically significant requires a three-way interaction between x, the source of variation, and group membership.There are many relevant examples in marketing of the use of moderation analyses to demonstrate a mechanism if there is a reason to believe the boundary of underlying process exists or the magnitude of the treatment effect varies by some observables. For example, after showing the European privacy regulation hurt online advertising, [47] ran a falsification check demonstrating that European consumers behaved like Americans when visiting American websites and that American consumers behaved like Europeans when visiting European websites. The paper then explored the mechanism and showed that the regulation especially hurt unobtrusive advertising and advertising on general interest websites, two situations where using data to target advertising is particularly valuable.Overall, mechanism checks through mediator or moderation analyses are important because they distinguish the goal of the marketing scholar from the marketing practitioner. Marketing practitioners run experiments and analyze data to understand what they should do in the particular situation they are facing. Marketing scholars need to have a broader sense of applicability beyond the specific setting being studied. Mediation and moderation analyses provide an understanding of when a marketing action will and will not lead to the desired behavior. For this reason, marketing papers are more likely to be remembered for the evidence that is shown in support of a theory explaining why the result holds. External Validity: How Generalizable is the Effect of x on y ?The external validity discussion in a paper should recognize the assumptions required for the analysis to capture the ATE across the population of interest, rather than a more local effect that is an artifact of the data sample or the source of quasi-experimental variation. A key concept is the ATE across the entire population. This is the difference in outcomes that would occur by moving the entire population from the control group to the treatment group. However, in some cases, the ATE may not be particularly relevant, because it averages across the entire population and includes units that would never be eligible for treatment ([100], p. 604). For example, we would not want to include millionaires in computing the ATE of a job training program. To address this, the researcher could use the average treatment effect on the treated, which measures the expected effect of treatment for those who actually were in the treatment condition.One reason why a research setting may fail to be externally valid is if the treated population is unrepresentative ([72]). A concern that will drive whether the treated population is unrepresentative is whether those affected could self-select into and out of the treatment. For example, [28] study a rule change by Google that allowed non–trademark holders to use trademarks in search advertising copy. They study the rule change's effect on user click behavior. In this case, many advertisers did not alter their advertising copy strategy, for a variety of reasons. These advertisers may be systematically different from the advertisers that did change their strategy. Because these advertisers were not forced to change their strategy, we will never know what would have happened if they did. When faced with such issues, it is best to spell out the potential for self-selection and discuss whether it makes the paper more or less relevant. In this case, it would be accurate to say that the researchers captured the effect of a loosening of trademark restrictions, because it is unlikely that a search engine would force its advertisers into using other advertisers' trademarks. However, it would not be accurate to claim that the researchers capture the broader effect of all advertisers using other advertisers' trademarks in their copy.The treated population may also be unrepresentative if the treatment impacts a subpopulation to change behavior, but not the main population of interest. This means that the measured effect is localized to that subpopulation, and it is referred to in the literature as the local average treatment effect (LATE). For example, in the context of regression discontinuity, the LATE is the average of the treatment effect over the individuals who would have been in the counterfactual condition if the discontinuity threshold were changed. A limitation of regression discontinuity is that the results directly apply only to populations around the threshold. For example, comparing the $49 spend with the $51 spend may be informative about the impact of the marketing incentive on consumers who spend around $50; however, consumers who typically spend a lot more or a lot less might be different. The idea of LATE also has implications for the interpretation of instrumental variables estimates, as any IV estimate is the LATE for the observations in the regression who experienced the kind of variation exploited by the instrument.[10]More broadly, as with other aspects of quasi-experimental research, the best practice regarding the external validity of results is to clearly lay out the assumptions and limitations. For example, [94] use a quasi-experiment and DID to examine the impact of advertising revenue on the type of content posted on Chinese blogs. While it might be tempting to interpret the results as suggestive of a broader impact of commercial interests on media, they are careful to emphasize the many differences between blogs and other media, between China and the rest of the world, and between the way the bloggers were compensated and other online advertising models. In this way, Sun and Zhu's article explicitly limits the temptation of the reader to extrapolate too much.An internally valid quasi-experimental estimate can have broader external validity when used to identify relationships such as elasticities and then to use a structural model to identify the counterfactual of interest. In these cases, under the assumption that the model is a useful representation of reality, quasi-experimental methods serve as a complement for, rather than a substitute to, structure. For example, [ 9] use quasi-experimental methods to identify the impact of the automotive brand preferences of parents on the brand preferences of their children. They then use structural methods to estimate the implications for firm strategy. [42] use quasi-experimental variation in health insurance prices to identify price elasticity and then combine this measure with a structural model to estimate the welfare implications of adverse selection. [29] use quasi-experimental variation around set quotas to identify the relationship between commissions and sales, and then use this variation in a structural model to determine optimal compensation schemes.Overall, effective quasi-experimental research requires an understanding of the underlying assumptions behind any broad interpretation of quasi-experimental results. Quasi-experiments often require a focus on a narrow slice of the data, and therefore, it is important to consider the degree to which the results apply to a broader population. Apologies: What Remains Unproven and What Are the Caveats?Any identification strategy relies on a set of assumptions. These assumptions need to be explicit throughout the paper. There are always some tests that cannot be run, for example, due to lack of data. There are always some robustness checks that are weaker than others. There are always some steps from data to interpretation. While apologies do not mean all is forgiven, the objective should be to clarify the boundaries of the claims. Obfuscation is much worse than a clear summary of the identifying assumptions.As an example, [51] employ a DID research design to study the effect of the payment disclosure law introduced in Massachusetts in June 2009. The research design uses the setting that physicians located in the border counties of Massachusetts and its neighboring states did not have disclosure laws during this period. They lay out the assumptions underlying their estimation:Our identification of the effect of disclosure legislation relies on the change in new prescriptions by physicians located in Massachusetts (MA) after the policy intervention, relative to their counterparts from ""control"" states in which no such law existed in the same period.... To assess potential threats to the validity of our research design, we verify if the result was driven by changes in physician payments as a result of the MA disclosure law. If such payment changes were primarily driven by local pharmaceutical reps reallocating their marketing budgets across physicians operating on either side of state borders, this would render the border identification strategy problematic.([51], p. 517)This example communicates three distinct points. First, it explains the identification strategy. Second, it details the main threats to the validity of this identification strategy. Third, it describes what they do to address it. These points suggest that effective apologies focus on demonstrating what interpretations are reasonable, and what might be a stretch of the results. The goal is not to show that in all circumstances and every conceivable way the identification is perfect. That is not possible. Instead, the goal is to provide clear bounds on the interpretation. The paper's contribution is then a function of whether it provides new knowledge under this bounded interpretation. ConclusionQuasi-experimental techniques are an important tool for marketers. First, marketing scholars need to be able to inform marketing practitioners—both managers and policy makers—about the causal effect to allow practitioners to make superior decisions. Second, the best quasi-experimental papers do not simply prove a causal effect but delve into the underlying mechanism, which is key to marketing scholarship's goal of generalizability. Third, such techniques become more important as the scope and span of marketing practice expands and there are new settings and more varied sources of data that allow their application.The objective of a quasi-experimental research paper is to answer an interesting and important research question about a causal relationship and provide evidence suggesting the mechanism behind the relationship. The choice of method (DID, regression discontinuity, or instrumental variables) depends on the nature of the quasi-experiment. The framework we present focuses on understanding how exogenous variation helps uncover causal relationships and why specific actions affect behavior. Of course the details of the methods will evolve over time as new research appears. Because marketing scholars are often interested in providing generalizable insights about how marketing actions change the behavior of individual consumers, the quasi-experimental framework is particularly useful. Similarly, firms that want to use those insights benefit. As the availability of detailed data grows and marketing technology changes, these methods will enable marketing scholars to provide assessments of a wide variety of situations in which a particular marketing action is likely to change consumer behavior or market dynamics. " 11,"Connecting to Place, People, and Past: How Products Make Us Feel Grounded"," Consumption can provide a feeling of groundedness or being emotionally rooted. This can occur when products connect consumers to their physical (place), social (people), and historic (past) environment. The authors introduce the concept of groundedness to the literature and show that it increases consumer choice; happiness; and feelings of safety, strength, and stability. Following these consequential outcomes, the authors demonstrate how marketers can provide consumers with a feeling of groundedness through product designs, distribution channels, and marketing communications. They also show how marketers might segment the market using observable proxies for consumers' need for groundedness, such as high computer use, high socioeconomic status, or life changes brought on by the COVID-19 pandemic. Taken together, the findings show that groundedness is a powerful concept providing a comprehensive explanation for a variety of consumer trends, including the popularity of local, artisanal, and nostalgic products. It seems that in times of digitization, urbanization, and global challenges, the need to feel grounded has become particularly acute.","To be rooted is perhaps the most important and least recognized need of the human soul.—[47], p. 43).Dual forces of digitization and globalization have made our social and work lives become increasingly virtual, fast-paced, and mobile, leaving many consumers feeling like trees with weak roots, at risk of being torn from the earth. In response, we observe consumers trying to (re)connect to place, people, and past—to get anchored. Against this backdrop, we propose and test an important driver of consumer behavior that has largely been overlooked in marketing literature: the feeling of groundedness.We believe that many consumers have a need to feel grounded, which we define as a feeling of emotional rootedness. This feeling emanates from connections to one's physical, social, and historic environment and provides a sense of strength, safety, and stability. Although the concept has received scant attention in prior marketing, consumer behavior, and social psychology research, the feeling of groundedness appears to be a familiar one among lay consumers. For example, we might feel grounded when returning to our birthplace, sitting at our grandparents' kitchen table while enjoying a pie made with apples from their backyard tree and according to a recipe passed down for generations. Similarly, we may have experienced feeling grounded when shopping at the local farmers market or foraging a basket of mushrooms from a nearby forest.We argue that there are at least three conceptually separable (but in practice often intertwined) sources of feelings of groundedness: connectedness to place, people, and/or past. Collectively, connections to place, people, and past engender feelings of groundedness by ""rooting"" us in our physical, social, and historic sphere. These connections may be established through many different objects, activities, and types of interactions. In this article, we focus on the role of products in providing customers with a connection to place, people, and past.Indeed, numerous marketplace examples illustrate increasing consumer demand for products that presumably make them feel more connected and thus grounded: Spearheading a renaissance of artisan, indie, and craft production, for example, locally rooted (micro)breweries have gained substantial market share in recent years. In 2019, craft beer accounted for 13.6% of total beer volume sales—a number that had increased by 4% even as overall U.S. beer volume sales had decreased by 2% ([ 6]). Similarly, sales estimates of local food increased from US$6.1 billion in 2012 to US$8.7 billion in 2015 ([24]; [45]) and farmers markets—which afford a connection to the land and to the people behind the food—are on the rise. In 2014, there were 8,268 farmers markets across the United States: a growth of 180% since 2006 ([24]). Beyond the food industry, online marketplaces such as Etsy connect consumers to handcrafted products and to the craftspeople that sell them. Impressively, Etsy reported 81.9 million users and US$10.3 billion gross merchandise sales worldwide in 2020 ([10]).This trend in demand for local, personal, and traditional products is surprising when considered against the backdrop of globalization, digitization, and modern society's penchant for technology and innovation. Marketers have begun to capitalize on these shifts in demand—for example, by stocking and promoting local products, encouraging contact with the people who make the products, and highlighting traditional ingredients or production methods. We have recently also observed marketers referring to the concept of groundedness. The Austrian grocery chain BILLA ran a national advertising campaign in fall 2020 referring to the farmers behind their products as ""The people who make us grounded"" (""Wer uns erdet"").In light of these trends, we contend that products can metaphorically connect us to place, people, and past, and thereby make us feel grounded. For brevity, we hereinafter refer to products that can make consumers feel grounded as ""grounding products."" We argue that the ability of products to provide a feeling of groundedness will make them more attractive to consumers. We further propose that feeling grounded may contribute to consumer well-being. Groundedness—understood as a feeling of deep-rootedness, having a strong foundation, and being securely anchored—gives consumers feelings of safety, strength, and stability as well as confidence that they can withstand adversity. As such, feelings of groundedness might provide consumers with a sense of happiness, thus adding to their overall well-being.This work makes several contributions. First, it introduces the feeling of groundedness as a driver of consumer behavior and consumer welfare. Second, it provides an overarching theoretical explanation for a variety of major consumer trends, such as the desire for local, craft, and traditional products. Third, it highlights that consumers experience a feeling of groundedness when products connect consumers to their physical (place), social (people), and historic (past) environment. Fourth, the studies offer various actionable marketing implications for products aimed at helping consumers connect to place, people, and past. Groundedness Groundedness in the LiteratureAs a personal characteristic, to be ""grounded"" is a common concept in everyday parlance, easily found in any dictionary. In contrast to everyday parlance, we found groundedness to be a fairly novel and underresearched construct in the literature. There are few direct references to groundedness in the marketing, consumer behavior, or social psychology literature streams. The mentions we did find in other literature (e.g., psychotherapy, environmental or educational psychology) are relatively obscure, only loosely related, or speculative (for an overview of relevant research, see Web Appendix A). For example, educational psychologist [29] writes about ""rootedness"" and develops a measure of rootedness for college students. However, McAndrew's explanation of rootedness is limited to location. Similarly, environmental psychologists (e.g., [27]; [33]) have studied connectedness to nature, which is also a more limited construct. We found a more closely related conception of groundedness in a psychotherapy doctoral dissertation, where [31], pp. 82–83) describes rootedness in terms of ""the personal, social, environmental, and economic anchoring that sees us through tough times. Within rootedness, there is a sense of togetherness, a combination of personal identity and group identity, past and present, and people and places.""In philosophy, [47], p. 43) points to the importance of being rooted. She notes:A human being has roots by virtue of his real, active, and natural participation in the life of a community, which preserves in living shape certain particular treasures of the past and certain particular expectations of the future. This participation is a natural one, in the sense that it is automatically brought about by place, conditions of birth, profession, and social surroundings.[12] likewise writes about rootedness in terms of the need to establish roots and feel at home in the world, while [41] refers to a connection to the land as a source of well-being that is undermined by technological forces that separate people from their roots in nature.In marketing, [42] examine rootedness in the context of community-supported agriculture (CSA), arguing that by connecting consumers to the land and producers, CSA membership may help consumers reconnect to their ""material, historical, and spiritual roots"" (p. 141). [ 2] also touch on some of the elements, antecedents, and consequences of groundedness, such as community and traditions.In summary, we believe the idea of groundedness has not been formally developed as a concept, nor have the full scope of the construct and its implications for consumer behavior and marketing been identified. We aim to fill this gap in the literature. The Construct of GroundednessWe argue that many consumers have a need to feel grounded, which we define as a feeling of emotional rootedness. The feeling of groundedness results from being metaphorically embedded in one's physical, social, and historical environment. Like the roots of a tree or the foundation of a house, a feeling of groundedness connects a person to their ""terroir"" (where the French word terroir not only refers to the land per se but also includes its cultural history and human capital [[35]]). Consistent with relevant dictionary definitions—which include being mentally and emotionally stable or firmly established[ 5]—we argue that the feeling of groundedness provides a solid foundation that imparts a sense of strength, safety, stability, and confidence that one can withstand adversity. Connection to placeConsistent with the idea of ""spreading one's roots into the ground,"" and the literal translation of terroir as ""land"" or ""soil"" ([35]), the feeling of groundedness can be obtained from a connection to a physical environment or place. This connection can be physical in the literal sense, as when working with actual, tangible objects that originate in the local environment, or when immersing in the natural environment itself. We find examples of such immersion in, and connection to, the natural sphere in the East Asian tradition of shinrin yoku, or forest bathing ([19]), and the Nordic cultures' idea of outdoor life (Friluftsliv), which, according to [15], p. 3), provides ""a biological, social, aesthetic, spiritual and philosophical experience of closeness to a place, the landscape, and the more-than-human world; an experience most urban people today lack."" In the same vein, connection to place may be experienced when directly drawing from the earth, as popularly pursued in urban gardening and farming. Indeed, one of [42], pp. 140–41) informants states, ""That's what farming actually is [a connection to the earth].... You are working with the living world. It's the connection you give people to the farm."" In addition to a physical connection, consumers can also connect to place in a more symbolic sense. They may do so, for example, by consuming locally produced goods, such as a beer from a nearby brewery. Establishing a connection to one's place to feel grounded may have become especially important as a consequence of migration and mobility. For example, a consumer who has recently been relocated to a certain town may particularly desire to consume products local to that town, thus enabling them to build a connection to that place. Connection to peopleFeelings of groundedness can also arise from a connection to one's social environment. Just as the meaning of terroir also includes its human capital ([35]), the idea of a ""place"" that provides groundedness, such as home, is often strongly shaped by the people and community associated with that place.In the social psychology literature, the human need for connectedness or belongingness to other people ([ 4]) has been well established. Running counter to that need is the phenomenon of modern-day alienation ([26]). The concept has been revived by marketing scholars to describe alienation of the consumer from the marketplace ([ 1]), and from a product's producer ([46]). Along the same lines, [ 2] observe postmodern consumers' feelings of personal meaninglessness and loss of moorings brought on by globalization and technology, while stressing the importance of identity, home, and community as antidotes to these feelings.Although the strongest route to groundedness via people might be connecting to one's closest social surroundings (e.g., one's family), we also see customers trying to reestablish a connection to people by means of certain product choices. Both online and offline, consumers may obtain groundedness by buying directly from the producer. At a farmers market, consumers may buy eggs directly from the person who fed the chickens and collected their eggs. On Etsy, online shoppers can order a breakfast mug from the very person who designed and shaped the piece with their own hands; the shopper might even be able to communicate directly with that person and learn how they developed their passion for handicraft. Either way, this enables the customer to get ""closer to the creator"" ([39]). On the business side, many firms, big and small, try to facilitate connections between customers and the people behind their products: for example, featuring individual producers on the packaging, indicating the name and address of food suppliers, or communicating via the company's founder or chief executive officer ([14]). Connection to pastThe human environment, or terroir, also includes a historical dimension ([35]). We suggest that feelings of groundedness can also be experienced based on a connection to the past. The past provides a foundation of memories, traditions, and cultural values for individuals to be grounded in.Examples from the marketing literature illustrate how consumption behavior establishes a connection to the past and begets feelings of groundedness. In [42], some respondent quotes suggest that community-supported farms provide not merely a connection with their local physical environment and the people around them but also a symbolic connection to past generations within one's own family (e.g., a connection to ancestors who were farmers). [44], who investigated Nordic consumers' food consumption motives, state that ""in the end, it is the caring food-producer who can bring the ubiquitous brand consumption back to where we were before industrialism"" (p. 230). Similarly, [ 3] find that visits to local farmers markets allow consumers to ""reconnect with their agrarian roots"" (p. 567), searching for ""food that is embedded in their personal and shared social histories"" (p. 564). In the consumer product domain, we see a resurgence of historic brands such as Converse ([23]) and observe companies helping consumers get connected to, or grounded in, the past. For example, firms may purposefully manufacture according to traditional and artisanal methods, such as making things by hand ([13]), or return to using older, often more ""natural"" materials and ingredients.Building on this conceptualization, our first prediction is as follows: H1: Products that connect consumers to place, people, and past provide consumers with the feeling of groundedness. How Groundedness Is Distinct from Related ConstructsProducts that connect consumers to place, people, and past frequently differ from other products in more aspects than their affordance of feelings of groundedness. For example, a local, traditional product is probably also more authentic ([32]; [34]). Likewise, products that connect to place, people, and past could be deemed higher quality or costlier to produce. They may be more unique ([25]), or perceived as made with love ([13]). Consumers may feel a stronger brand attachment to such products ([43]). These products may also provide a greater sense of human contact ([38]), brand experience ([ 5]), brand community (e.g., [28]), and sense of nostalgia ([ 9]). Products that provide a feeling of groundedness may also evoke a feeling of being true to oneself (i.e., self-authenticity or existential authenticity; e.g., [ 2]; [16]), a feeling of knowing who one is (self-identity), a general sense of belonging ([ 4]) that is not about feeling grounded and deep-rooted, or a general sense of meaning in life ([21]; [37]; [40])—all of which could increase one's well-being.While these related constructs are relevant, we argue that they play different conceptual roles than groundedness. First, some constructs—such as product authenticity, product quality, or product uniqueness—are characteristics of products. They logically cannot cast doubt about the existence of groundedness, which is a feeling about the self.Second, other alternative constructs could be classified not as characteristics of brands but as feelings about brands. For example, brand attachment is a feeling of connection to a brand. In some situations, feeling connected to a brand might be a consequence of a brand's relationship to a place, people, or the past that a consumer longs to feel a connection with. For example, a consumer may be more likely to feel attached to a wine brand from their own region (or to their favorite laptop brand, which may have nothing to do with feeling connected to place, people, or past). However, this feeling of brand attachment is not a feeling about the self. Thus, it cannot be the same as the feeling of groundedness.A third category of constructs relates to connectedness but is focused on only one of the three sources. For example, nostalgia, as ""a sentimental longing or wistful affection for a period in the past,""[ 6] is related to the past but not necessarily people or place. Likewise, these constructs might be alternative explanations for one of the antecedents of groundedness but not groundedness itself. In addition, nostalgia describes a state of longing or affection, but it does not stipulate that this longing has been satisfied by an actual connection to the past. Thus, nostalgia is conceptually more closely related to the need for groundedness than to actually feeling grounded.Finally, there are some constructs involving feelings about the self that might be driven by similar antecedents or generate similar consequences as the feeling of groundedness; these include feeling true to oneself (i.e., self-authenticity), a sense of belonging that does not involve a feeling of deep-rootedness, a sense of self-identity, and a general sense of meaning in life. Our studies will assess these alternative constructs to groundedness. FrameworkFigure 1 depicts our conceptual framework. At the core of this framework and as summarized in H1 is that there are at least three immediate sources of groundedness: connection to the physical environment, or to place; connection to the social environment, or to people; and connection to the historical environment, or to the past.Figure 1 further depicts our hypotheses about the consequences of the feeling of groundedness; in particular, we consider product attractiveness (H2) and consumer well-being (H3) as important outcome variables. We then examine ways in which marketers can leverage groundedness on the basis of marketing-mix elements (H4) and consumer characteristics (H5).Graph: Figure 1. Conceptual framework. How Groundedness Affects Consumer ChoiceIn our predictions about downstream effects of groundedness, we hypothesize that groundedness increases product attractiveness and, thus, affects consumer choice. In particular, we suggest that products providing a connection to place, people, and past beget feelings of groundedness for the customer and may therefore be more attractive than their competitors that do not. We thus predict that customers will prefer these products and have stronger intent to purchase and higher willingness to pay (WTP). More formally, H2: Products' ability to provide consumers with the feeling of groundedness makes those products more attractive to consumers. How Groundedness Affects Consumer Well-BeingBeyond marketplace outcomes, we hypothesize in our predictions that groundedness increases consumer well-being. In particular, we suggest that feeling grounded provides consumers with a sense of strength, stability, safety, and confidence in one's ability to withstand adversity. As such, feelings of groundedness might provide consumers with a sense of happiness, thus adding to their well-being. We find conceptual support for these predictions in the descriptions of [31] and [42]. [31], p. 82) refers to rootedness as providing ""a sense of balance, belonging, and fitting to one's place."" Further specifying the elements of well-being afforded by groundedness, Ndi (p. 59) says that rootedness is ""the ultimate feeling that provides stability, harmony, and happiness among people and their community,"" whereas a lack of rootedness leaves a person with a sense of meaninglessness, disconnectedness, emptiness, vulnerability, and unhappiness. Building on [41] work in biodynamics, [42], p. 140) also suggest that emotional connections to one's environment ""are a primordial source of spiritual sustenance and a foundation of social and personal well-being and, conversely, that psychological and societal unrest are precipitated by technological forces that separate humanity from its roots in nature."" Research on constructs related to groundedness also provides indirect, suggestive evidence for our proposition that groundedness increases consumers' well-being. [27], for example, find that connectedness to nature is positively correlated with subjective well-being. We predict the following: H3: The feeling of groundedness increases consumers' subjective well-being. How Marketers Can Leverage Groundedness Marketing-mix strategiesMarketers can use several marketing-mix variables that help connect consumers to place, people, and past and thus make them feel grounded. Marketers can promote the location where the product is made or ingredients are sourced, engage in storytelling about the history of the brand, or introduce the people who produce the products ([14]; [46]). Marketers can design products in a local or traditional style; use local, ethnic, or traditional ingredients; or employ traditional production processes (e.g., in ""indie"" products). Marketers can also adjust their channels of distribution to help customers connect to place, people, and past. For example, farms and small producers can use farmers markets (vs. supermarkets) that connect consumers with place, people, and past. Retailers can employ traditional store designs or focus their assortments on more traditional products. We propose the following: H4: Marketing-mix variables such as communication, product design, and channels of distribution can be designed to increase the feeling of groundedness. Consumer segmentation strategiesWe expect that consumers differ in how important feelings of groundedness are to them. That is, the level of need for connection with place, people, and past, and thus, for groundedness, varies across consumers. We examine three reasons why the need for groundedness might be heightened in certain consumer segments. First, the need for groundedness should be particularly strong when consumers' life and work make it difficult to establish and maintain strong connections with place, people, and past. We suggest that living in large cities (which are often inhabited by people who did not grow up there, are characterized by social anonymity, and tend to showcase modernity) is a predictor of need for groundedness. With regard to work, we expect that performing mostly computerized work, confined to the limits of one's desktop, puts a distance between individuals and other people as well as the physical environment. We consequently argue that computerized work is associated with a stronger need for groundedness.Second, we propose that the need for groundedness is stronger when consumers' foundations are shaken or connections with place, people, and past are severed or under pressure. We expect this to have been the case, for example, during the COVID-19 pandemic, a global event that indeed disrupted many people's lives. Accordingly, those who the pandemic had more strongly put in a state of flux should have experienced a higher need for groundedness.Third, we suggest that the need for groundedness will be more prominent for consumers whose more basic needs are satisfied. Respective proxies such as consumers' socioeconomic status (SES) should thus be correlated with their felt need for groundedness. We predict the following: H5: The feeling of groundedness is more important to consumers when their work and life do not provide a strong connection to place, people, and past; when life events shake their foundation; or when their basic needs are already sufficiently met. Overview of StudiesWith a view to robustness and generalizability, we test our predictions in eight experiments and one consumer survey, based on a variety of samples and data collection techniques (students in behavioral labs at universities, online platforms, and professional market research panels, both in the United States and in Europe). For managerial usability, our study paradigms include both consequential outcome measures as well as marketing-relevant factors that can be manipulated or measured. Study 1 provides evidence that groundedness increases product attractiveness in real economic terms using an incentive-compatible measure of WTP. Studies 2a–c show that groundedness has explanatory value above and beyond alternative constructs. These studies also explore how a product's affordance of groundedness depends on the closeness of the consumer's connection to the provenance of the product or the producer of the product. Studies 3 and 4 provide concrete implications for marketing practice by manipulating product design and assortment, showing how demand for traditional versus innovative products is affected by consumers' current need for groundedness, and exploring proxies that might allow managers to assess said need. In Studies 5a and 5b we focus on psychological effects on consumers. Study 5a shows that groundedness has a positive effect on consumer happiness, whereas Study 5b examines the effect of a grounding product on one's feelings of strength, stability, and safety. Study 1: Groundedness and Product AttractivenessStudy 1 tests the effect of groundedness on product attractiveness (H2). We do so in a study paradigm that aims to showcase the managerial relevance of the focal effect. Specifically, we exposed participants of a consumer panel to a more grounding ""indie"" brand of soap versus a less grounding industrial brand and took an incentive-compatible measure of participants' WTP for each product. We separately tested the extent to which the two brands provide a connection to place, people, and past (see Web Appendix B). We also measured a moderator—importance of the product category to the consumer—to provide further insight into the process and strengthen internal validity (e.g., to alleviate any concerns about demand effects). We reasoned that the self-related benefit of groundedness afforded by indie (vs. industrial) brands should be more pronounced when the product category is more central to the self (i.e., more important to the consumer). MethodAn age- and gender-representative sample of 311 Austrian consumers from a professional market research panel participated for monetary compensation (Mage = 41.8 years; 50.2% female; for instructions and stimuli of this and all following studies, see Web Appendices B–F). All participants were exposed to a color picture and verbal description for two bars of soap. An almond-scented soap made by Firm A was always presented on the left. An olive-scented soap from Firm B was always presented on the right. We manipulated which firm was described as indie (""makes high-quality products that are produced in a small and independent craft business"") versus industrial (""makes high-quality products that are industrially produced at scale in a large factory"").[ 7] Participants indicated their WTP for a bar of soap from both companies separately using an incentive-compatible elicitation method (dual-lottery Becker–DeGroot–Marschak procedure; e.g., [13]). This method provides an incentive-compatible measure of what the product is worth to participants.Next, participants indicated which soap provided relatively stronger feelings of groundedness by rating agreement with the following two statements (translated from the original German): ""When I think of this firm's soap ... I feel deep-rooted and firmly anchored ('grounded')"" and ""I can firmly feel my feet on the ground."" Participants also indicated how well a graphic depicting a human form with branches for arms and a deep, wide root system instead of legs (see Figure 1) represented their emotional state. The three items were measured on a seven-point scale (1 = ""true for Firm B,"" and 7 = ""true for Firm A"") and were averaged to create a groundedness index (α = .87).[ 8] We captured the importance of the underlying product category to the consumer with a three-item measure (e.g., ""The product category 'soap' is very important to me""). All measurement items used in this and subsequent studies, as well as their reliability statistics, are listed in Web Appendices B–F. Unless indicated differently, items are measured on seven-point scales (where ""strongly disagree/does not describe my feelings at all/not true of me at all/true for Brand B,"" etc. is coded as 1, and ""strongly agree/describes my feelings very well/very true of me/true for Brand A,"" etc. is coded as 7). Results and DiscussionWe ran a repeated-measures analysis of variance (ANOVA) with consumers' WTP in euros as the repeated-measures factor and our indie versus industrial counterbalancing manipulation as the between-subjects factor (for complete results, see Web Appendix B). We find the expected interaction effect (F( 1, 309) = 174.51, p < .001). Follow-up contrast analyses show that participants are willing to pay more for the soap of Firm A if that product is portrayed as an indie (Mindie = €3.29) versus as an industrial (Mindustrial = €1.91; F( 1, 309) = 37.47, p < .001) brand. Likewise, the soap of Firm B is valued more when Firm B is described as an indie (vs. industrial) company (Mindie = €3.12, Mindustrial = €2.11; F( 1, 309) = 20.67, p < .001)—a notable 60% increase in value. For moderation and mediation analyses, we calculated the intraindividual delta WTP (WTPFirm A − WTPFirm B: MFirm A indie = €1.18, MFirm A industrial = −€1.21; F( 1, 309) = 174.51, p < .001).An ANOVA on the groundedness measure indicates a significant effect: when Firm A is described as indie, participants more strongly declare that Firm A makes them feel grounded (MFirm A indie = 5.15) compared with when Firm A is described as industrial (MFirm A industrial = 2.92; F( 1, 309) = 269.58, p < .001). Mediation analysis ([20], Model 4, 10,000 bootstrap samples) shows that the WTP effect is mediated by feelings of groundedness (indirect effect = 1.24, 95% confidence interval [CI95%]: [.87, 1.67]). A moderation analysis ([20], Model 1) with the delta WTP measure as dependent variable confirms the hypothesis that the indie premium increases as the category importance increases (p < .001; for details, see Web Appendix B). Finally, a moderated mediation analysis ([20], Model 8) shows that this interaction effect is mediated by groundedness: the indirect effect of indie versus industrial on delta WTP through feelings of groundedness is always significant but stronger at high versus low levels of category importance (indirect effect16th percentile = .79, CI95%: [.51, 1.12]; indirect effect50th percentile = 1.13, CI95%: [.77, 1.55]; indirect effect84th percentile = 1.54, CI95%: [1.04, 2.16]; index of moderated mediation = .21, CI95%: [.11,.34]).Study 1 finds that products making a connection to the past, to people, and to a place make consumers feel more grounded, which increases their WTP. Thus, the result in Study 1 supports H2. The effect is managerially relevant: the more grounding product yielded a notable 60% increase in WTP. In addition, Study 1 shows that the effect is moderated by the importance of the product category. The pattern of moderated mediation, where the indie versus industrial nature of the brand is less important to feelings of groundedness when the product is less important to the consumer's identity, provides further evidence for our process.A limitation of Study 1 is that indie versus industrial products may differ in more aspects than their ability to provide a feeling of groundedness. For example, an indie brand might provide higher value to consumers by being perceived as more authentic ([32]) and more unique ([25]) than an industrial brand. Further, the description of the indie brand and its production method might give consumers a greater sense of love ([13]), human contact ([38]), attachment ([43]), brand experience ([ 5]), and brand community (e.g., [28]). Or the indie brand might simply be higher quality and costlier to produce. Our mediation and moderated mediation provide initial evidence for the proposed groundedness process, suggesting that these alternative processes are not the only drivers of the effects on WTP. We explicitly address these alternative explanations in Study 2. Study 2: Connectedness to Place or People, Groundedness, and Product AttractivenessOne major element of our theory is that the feeling of groundedness afforded by a product results from the connection that product provides to place, people, and past (H1). If products are indeed connectors between customers and their place, people, and past, we should be able to affect groundedness—and product attractiveness (H2)—not just by manipulating the place, people, or past of the product as we did in Study 1 but also by manipulating the place, people, or past of the customer. Thus, in Study 2a, we keep brands and products constant and manipulate how much groundedness a brand is able to provide as a function of a customer characteristic (i.e., customer location), rather than a product characteristic. Study 2a MethodWe asked 172 students (Mage = 21.9 years; 79.7% female) at a Northeastern U.S. university (n = 89, for a gift voucher and cookies) and an Austrian university (n = 83, for course credit) to imagine that they had just moved to either Karlstad or Umeå in Sweden. We then asked them to choose (using a three-item measure, e.g., ""Which of the two craft beers do you choose?"") which of two real Swedish craft beer brands, Good Guys Brew from Karlstad and Beer Studio from Umeå, they would purchase on their first night out. Next, participants reported which of the two brands they perceived would make them feel more grounded (""In the situation described, this brand would make me feel deep-rooted,"" ""This brand would make me feel well-grounded,"" and ""In a metaphorical sense: Which of the two craft beers would rather make you feel as illustrated by the following picture?"" [showing the picture of a human/tree form with deep roots]; α = .90). All items in this study were captured on seven-point scales where one anchor was the beer from Karlstad and the other anchor the beer from Umeå. We counterbalanced which beer was shown on the left- versus right-hand side. Before the participant location manipulation, we also asked participants to rate the two brands on a relative scale regarding nine product characteristics that might make either product more attractive. Because these were product characteristics that should not have been influenced by the participant's location, and because they were measured before the location manipulation, they did not—and could not—explain our results (for results regarding the control variables in this and all subsequent studies, see Web Appendices C–F). At the end of the study, we captured some information about the participants' relation to beer and to Sweden (e.g., ""Have you ever been to Sweden?,"" ""How much do you like beer in general?"").[ 9] Results and discussionA one-way ANOVA shows that participants who moved to Karlstad prefer the Karlstad-based beer significantly more than those who moved to Umeå (MKarlstad = 4.80, MUmeå = 4.14; F( 1, 170) = 6.70, p = .010). Similarly, the Karlstad-based beer provides relatively more groundedness to participants who moved to Karlstad versus Umeå (MKarlstad = 4.29, MUmeå = 3.79; F( 1, 170) = 5.77, p = .017). Groundedness mediates the effect of residence location on preference (indirect effect = .40, CI95%: [.07,.74]; [20], Model 4). For each of the nine alternative constructs, the focal indirect effect via groundedness remains significant when we include the alternative construct as a rival mediator.Study 2a shows that groundedness drives product attractiveness (H2) when we keep products constant but manipulate the place of the customer. This study highlights that the groundedness effect depends not only on the features of the product but also on the situation of the customer. Managerially, the study shows that local brands are particularly grounding and thus attractive to local consumers. Study 2a manipulated how participants relate to a place that is connected to a focal product, and thus how much groundedness it affords them. Unlike Study 2a, Study 2b capitalizes on participants' existing relationship to a place. Study 2b further addresses alternative constructs to groundedness by measuring them after the focal manipulation. Study 2b MethodThe week before Christmas, we asked 1,306 Austrian students from a university in Vienna (Mage = 22.8 years; 55.4% female; compensated by a lottery for an iPhone 11 and five €10 gift vouchers, prescreened for having grown up in Austria but outside Vienna and for celebrating Christmas) to imagine they were celebrating Christmas in Vienna this year and looking to buy a Christmas tree at a local market. We then varied between-subjects whether the market's Christmas trees originated from the state the participant grew up in or from a randomly selected other Austrian state. The trees were thus not connected to participants' current place (i.e., where they were studying and buying the tree) but to either the place where they grew up or a third location in Austria. Then, we assessed purchase intent for the Christmas tree using four items (e.g., ""I would very much like to buy a Christmas tree at this market""). We next captured feelings of groundedness from purchasing a Christmas tree at that market, using the same three items as in Study 2a. Finally, participants completed two-item measures of alternative constructs (the product's authenticity, uniqueness, quality, love, production costs, sense of human contact, brand experience, feeling of belonging to a brand community, and attachment). In addition, we measured participants' desire to support the producer as a possible alternative explanation. Due to this study's use of multiple items for each construct, we were able to ascertain that groundedness is empirically distinct from the other constructs captured (purchase intent and alternative constructs) using the [11] criterion. We performed the same tests in all subsequent studies with multi-item measures of our dependent variables (see Web Appendices C–F). Results and discussionParticipants are more intent on buying a Christmas tree from the focal market if it is from their own state (Mown place = 5.35) versus another state in the same country (Mother place = 4.95; F( 1, 1,304) = 24.27, p < .001). Further, when the trees originate from participants' own state, participants experience stronger feelings of groundedness than when the trees are from another state (Mown place = 3.39, Mother place = 3.15; F( 1, 1,304) = 8.43, p = .004), which is in line with H1. We do not find significant differences between conditions with regard to the alternative explanations captured (ps >.087). Differences in perceived production costs (Mown place = 4.17, Mother place = 4.30; F( 1, 1,304) = 2.92, p = .088) are marginally significant but run in the opposite direction of the dependent variable. Thus, they are unable to explain our results. Consistent with H2, a mediation model ([20], Model 4) shows that groundedness mediates the treatment effect on purchase intent (indirect effect = .11, CI95% [.03,.18]). For each of the ten alternative constructs, the focal indirect effect via groundedness remains significant when we include the alternative construct as a rival mediator.Studies 2a and 2b show that a product that connects a consumer to a place they relate to (a city they move to, the state they are from) makes them feel more grounded and is more attractive than a product originating from a specified place they do not relate to (another city or state in the same country). One pertinent question is how much that feeling of groundedness depends on the closeness of the connection to place, people, and past. While the more grounding option in Studies 2a and 2b connects customers to their own (""my"") current or past place, the indie brand utilized in Study 1 merely provided a connection to ""a"" place (and ""the"" people who made it and ""the"" past, respectively). Our view is that, ceteris paribus, the depth of groundedness gradually increases with the closeness of the connection. The closer the personal relationship of the customer to the place, people, and past represented by the product, the stronger the connection and thus feelings of groundedness established via the product. We test this prediction in the context of a customer connecting to the people dimension next. Study 2cStudy 2c addresses whether differences in closeness indeed matter—that is, whether they afford different levels of feelings of groundedness when compared directly. Beyond that, the study isolates connection to people as a potential driver of groundedness (H1). MethodTwo hundred U.K. crowd workers on Prolific (Mage = 33.8 years; 55.0% female; for monetary compensation) were asked to indicate their feelings of groundedness associated with the use of a coffee mug (using the same measure as in Studies 2a and 2b). To sample different levels of personal closeness along the proposed continuum, the producer of the mug was manipulated to be either ""an artisan that is personally close to you (e.g., a close friend, relative, partner, etc.)"" or ""an artisan that is a distant acquaintance of yours (e.g., a colleague from work, a neighbor, a friend of a friend, etc.)."" We measured perceived connection to people through the mug using three items (e.g., ""Drinking from this mug, I somehow feel a connection to 'my people'""). We used the same control measures as in Study 2b (except for the motivation to provide financial support by purchasing a product, given that there was no purchase in this study). Results and discussionFirst, the pattern of results for groundedness and connection to people supports our theorizing about a continuum of closeness and, thus, groundedness: perceived connection to people is significantly higher when the artisan producer is a close other versus when they are merely an acquaintance (Mclose = 4.34, Mdistant = 3.75; F( 1, 198) = 6.63, p = .011). The same is true for feelings of groundedness: participants experience stronger feelings of groundedness when considering the coffee mug produced by an artisan that is a close other versus one that is merely a distant acquaintance (Mclose = 4.14, Mdistant = 3.29; F( 1, 198) = 14.78, p < .001). Further, a mediation model ([20], Model 4) shows that producer closeness mediates the effect on groundedness (indirect effect = .42, CI95%: [.09,.75]). Importantly, for each of the nine alternative constructs, the focal indirect effect via groundedness remains significant when we include the alternative construct as a rival mediator.Thus, Study 2c shows that being personally closer to one of the sources of groundedness enables consumers to experience stronger feelings of groundedness. More precisely, groundedness is a function of how close the consumer's relationship is to the product's place, people (e.g., the product's producer), or past. As for different routes to groundedness, the study shows that a product's people dimension alone (e.g., its producer) can boost groundedness via a stronger perceived connection to people established by the product. Managerially, the findings are important because marketers can choose the extent to which they highlight the closeness or similarity between customers and producers. In addition, the study highlights that managers may need to search for personally relevant and close sources of groundedness from the perspective of a given target customer.The next set of studies investigates how the groundedness effect can be leveraged via marketing-mix elements (Studies 3a and 3b) and which types of customers have a particularly high need for groundedness (Studies 3a and 4). Study 3: Marketing Mix, Connectedness to Past, Groundedness, and Product AttractivenessStudy 3a focuses on connections to past as a source of groundedness (H1) by manipulating product design (H4). We also examine how the effect of groundedness on product attractiveness (H2) varies across consumers by capturing their chronic need to connect to the past (the higher this need, the stronger the groundedness effect should become). Study 3b manipulates consumers' state need for groundedness and addresses category management considerations by testing how consumers' need for groundedness impacts the preference for traditional versus innovative products. Study 3a MethodWe showed 223 students in the behavioral laboratory of a large European university (Mage = 23.9 years; 65.5% female; for monetary compensation or course credit) two sets of cutlery (from Brand A and Brand B) side by side, stipulating that they were of comparable price and quality. We manipulated product design to provide more versus less connection to the past by using a more traditional versus modern product design. We manipulated which set of cutlery was presented on the left- versus right-hand side (i.e., as Brand A vs. B). Using adapted versions of the measures in Studies 1 and 2, we asked participants to indicate which of the two brands they would rather purchase, which would make them feel more grounded, and which evoked a stronger connection to the past. Need to connect to the past as a chronic consumer trait—our moderator—was measured in terms of agreement with three items (e.g., ""I generally try to see if I can somehow satisfy my desire to [metaphorically] 'connect to the past'"").[10] Results and discussionOur manipulation proved effective: Participants more strongly associate Brand A ( = 7, Brand B = 1) with a connection to the past when Brand A cutlery had a traditional design (MBrand_A_traditional = 5.49, MBrand_A_modern = 1.97; F( 1, 221) = 405.25, p < .001). As expected (H4), we find a significant effect on groundedness—Brand A is perceived to provide more groundedness (relative to Brand B) when Brand A features traditional design (MBrand_A_traditional = 4.39, MBrand_A_modern = 3.65; F( 1, 221) = 18.50, p < .001). For product preference, we find an overall preference for the modern cutlery (MBrand_A_traditional = 3.69, MBrand_A_modern = 4.48; F( 1, 221) = 9.01, p = .003; of course, the fact that traditional products provide a stronger sense of groundedness does not preclude that many people might still prefer a specific set of modern cutlery over a specific set of traditional cutlery, or modern designs over traditional ones in general). More importantly, and as expected (H2), we find a positive effect of groundedness on product preference (b = .61, p < .001), and a positive indirect effect ([20], Model 4) of traditional (vs. modern) design on preference through groundedness (indirect effect = .55, CI95%: [.27,.89]). As one would expect, preference becomes even stronger for the modern cutlery when the groundedness path is controlled for (estimated MBrand_A_traditional = 3.40, estimated MBrand_A_Modern = 4.74).As anticipated, we find that one's general need to connect to the past significantly moderates purchase preference (p < .001; [20], Model 1). Thus, participants with a low need to connect to the past have a more pronounced preference for the modern cutlery; conversely, participants with a high need to connect to the past show a preference for the traditional cutlery (e.g., at need to connect to past = 1, conditional effect = −2.53, CI95%: [−3.58, −1.48]; at need to connect to the past = 7, conditional effect = 1.29, CI95%: [.002, 2.57]). A moderated mediation analysis ([20], Model 58; see Web Appendix D) shows that traditional design affords a stronger feeling of groundedness, and that groundedness becomes a more important driver of preference as general need to connect to the past increases. In fact, at very low levels of general need to connect to the past, a product's ability to provide feelings of groundedness no longer significantly impacts product preference (e.g., at need to connect to the past = 1, conditional effect = .33, CI95%: [−.05,.71]).In summary, Study 3a shows that by varying a marketing-mix element (product design) to be more traditional (vs. modern), marketers can affect customer preference via feelings of groundedness. This is because the marketing-mix element directly caters to a source of groundedness (H4). Study 3bStudy 3b investigates preference for traditional versus innovative products as a direct function of consumers' current need for groundedness and manipulates this need. We also perform a test of how the relative interest in different product categories—traditional versus innovative—is affected by different levels of need for groundedness, pointing to potential boundary conditions of the groundedness effect. MethodTwo hundred crowd workers on Prolific (Mage = 33.4 years; 54.0% female) from the United Kingdom took part in this study for monetary compensation. Participants filled out two ostensibly unrelated surveys. The first manipulated participants' current need for groundedness. Participants in the high-need condition read, ""Research has shown that feelings of groundedness can be positive or negative depending on the context and situation we are in."" They were then asked to describe a recent situation where feeling grounded was desirable to them because ""you metaphorically felt your roots were too loose and weak with respect to your connection to a place, to people, and the past."" Conversely, participants in the low-need condition read, ""Research has shown that feelings of groundedness can be negative or positive,"" and were asked to describe a situation where groundedness was undesirable to them because ""you metaphorically felt your roots were too dense and strong."" After completing the writing task and reporting their current need for groundedness on a version of our three-item groundedness scale, participants were thanked and told they would be forwarded to another study. Here, participants were introduced to two different online stores, presented side by side: one specializing in ""the best traditional products"" and one specializing in ""the best innovative products."" We then asked participants to indicate which of the stores they would prefer to shop at on a seven-point scale, with Store A and Store B as anchors. We alternated which of the stores (A vs. B) was presented as traditional versus innovative in our stimuli. We subsequently reversed the Store A versus B preference scores for half the data set, so that the innovative store preference was always anchored at 1 and the traditional store preference was always anchored at 7. Results and discussionOur manipulation was effective: participants who wrote about a situation where their need for groundedness was high reported experiencing a higher need for groundedness (M = 5.25) than those who wrote about a situation where need for groundedness was low (M = 4.11; F( 1, 198) = 41.19, p < .001). In terms of shopping preferences, participants in the high-need-for-groundedness condition showed a stronger preference for the online store with traditional (vs. innovative) products (M = 4.00) than those in the low-need-for-groundedness condition (M = 3.47; F( 1, 198) = 4.17, p = .043).Thus, and in line with H4, Study 3b shows that relative interest in purchasing traditional products is higher in situations and contexts where consumers' need for groundedness is high. In situations and contexts where groundedness is less sought after, innovative products become relatively more interesting. Study 4: Consumer Characteristics and Need for GroundednessStudies 3a and 3b suggest that groundedness is not equally attractive and relevant to all consumers in all situations. For segmentation purposes, it is important to know which consumers are more likely to have a strong enduring need for groundedness. As predicted in H5, we argue that the feeling of groundedness is more important to consumers when their work and life (e.g., computerized desktop work, living in a large city) do not provide a strong connection to place, people, and past; when certain life events (e.g., the COVID-19 crisis) shake their foundation; or when their basic needs are already sufficiently met (e.g., when they have higher SES). In Study 4, we use a survey to measure these consumer characteristics, along with need for groundedness and preference for products that connect to place, people, and past. The study was conducted in spring 2020, at the beginning of the COVID-19 pandemic and first lockdown. This enabled us to assess the impact of a disruptive life event on the need for groundedness. MethodAn age- and gender-representative sample from a U.S. consumer panel completed this survey for monetary compensation (N = 325; Mage = 45.5 years; 51.1% female). We first measured product preference and need for groundedness: preference for products connected to one's place, people, and past were measured (in random order) using three items each (e.g., ""I like to purchase products that connect me to 'my place' ['my people'/'my past'], i.e., my physical [social/historic] environment""). We merged these into one global index of purchase interest. Need for groundedness was measured using a version of our three-item scale, adapted to measure general need for groundedness (e.g., ""In general, I want to feel deep-rooted""). We next captured a series of demographic and lifestyle variables.To assess a potential lack of connection to people, place, and past in consumers' work and social lives, we captured three variables. First, we asked respondents about the type of area they live in (1 = ""in the countryside,"" and 7 = ""in a big city""). We hypothesized that living in large cities (which are often inhabited by people who did not grow up there, are characterized by social anonymity, and tend to showcase modernity) is a predictor of need for groundedness. Second, we assessed participants' desktop work using two items (e.g., ""During the week [e.g., when being at work] ... I primarily work at the computer""). We expected a positive relationship between desktop work and need for groundedness, because a disproportionate amount of computerized work (while confined to one's desktop) separates individuals from other people as well as the physical environment. A similar logic might apply to people whose job is characterized as ""work of the head"" (i.e., work that contains many abstract tasks), as opposed to people who perform manual labor (""work of the hands"") or work in social jobs (""work of the heart""; [17]). Respondents accordingly indicated which of these three categories their current or most recent job fell into.Next, to assess a potential link between need for groundedness and a disruptive major life event, we examined perceived impact of the COVID-19 crisis on the consumer's life. We assessed this with a single item (""Due to the current Corona [COVID-19] crisis, I feel that my life is in a state of major change""). Last, we theorized that the need for groundedness should become more prominent when basic needs such as food and shelter are not a concern. Therefore, we tested whether higher SES (measured on a three-item scale [e.g., ""I have enough money to buy things I want""]) might be an effective proxy for one's need for groundedness. No other measures were taken. Results and DiscussionFirst, and as expected, we find a significant and positive correlation between one's need for groundedness and purchase intent for products connecting to place, people, and past (r = .57, p < .001). Second, we analyzed the correlations of all proposed indicators with the need for groundedness. In particular, need for groundedness correlates positively with desktop work (r = .26, p < .001), SES (r = .30, p < .001), change experienced as a result of COVID-19 (r = .12, p = .030), and living in a big city rather than the countryside (r = .10, p = .079), but correlates negatively with performing work of the hands (r = −.11, p = .040; for a complete correlation table of this study; see Web Appendix E).Third, we ran multivariate ordinary least squares (OLS) regressions with all predictor variables on both need for groundedness and purchase intent. For those variables that emerged as significant predictors for both the need for groundedness and purchase intent, we examined whether the need for groundedness mediates the respective effects on purchase intent while entering all other variables as covariates. For conciseness, we report only significant results hereinafter (see Table 1 for details).GraphTable 1. Multivariate OLS Regression Models (Study 4). 1 *p < .05.2 **p < .01.3 ***p < .001.4 Notes: Mediation models ([20], Model 4): Mediator = need for groundedness, DV = purchase interest; ( 1) IV = SES: indirect effect = .10, CI95%: [.05,.15]; ( 2) IV = desktop work: indirect effect = .07, CI95%: [.03,.12]; ( 3) IV = change through COVID-19: indirect effect = .05, CI95%: [.002,.10].The multivariate OLS models showed that three predictors remain significant for both the need for groundedness (NG) and purchase intent (PI) when simultaneously including all variables in the model: ( 1) desktop work (NG: b = .14, SE = .04, t(316) = 3.89, p < .001; PI: b = .24, SE = .04, t(316) = 5.91, p < .001), ( 2) SES (NG: b = .19, SE = .04, t(316) = 5.01, p < .001; PI: b = .29, SE = .04, t(316) = 7.04, p < .001), and ( 3) change related to COVID-19 (NG: b = .09, SE = .04, t(316) = 2.30, p = .022; PI: b = .23, SE = .04, t(316) = 5.36, p < .001). Need for groundedness mediates the effect of all three variables on purchase intent (in line with H2; see Table 1 and Web Appendix E).Our ""work of the head"" dummy was not significant in the multivariate OLS model. We conclude that the ""work of the head/heart/hands"" measure was probably too rough and thus unable to adequately detect the important nuances in job characteristics that affect the need for groundedness. We were also surprised that one's living environment did not emerge as a significant predictor for need for groundedness in the multivariate OLS model. A closer look at the data reveals, however, that a disproportionately large number (29.2%) of respondents in our sample indicated living in big cities (i.e., chose the endpoint of the scale). When dichotomizing the measure (i.e., living in big city vs. not), we find the predicted positive effect: people living in a big city have a heightened need for groundedness (see Web Appendix E).In summary, Study 4 finds that a higher need for groundedness is apparent in consumer profiles characterized by larger societal trends: living in big cities (urbanization), doing desktop work at the computer (digitization), and undergoing major change (such as during the COVID-19 pandemic). Further, groundedness seems to be more relevant for high-SES consumers.Thus far, we have provided a cohesive picture of groundedness in terms of both triggers (H1) and market-relevant outcomes (H2), as well as ways for marketers to leverage groundedness (H4, H5). In the final two studies, we examine the implications of groundedness for consumers' psychological well-being (H3). Study 5: Connectedness, Groundedness, and Consumer Well-BeingTo test our hypothesis that feeling grounded increases consumers' subjective well-being (H3), Study 5a measures happiness as a consequence of attaining groundedness. We also test another managerial manipulation: channel type (H4). Study 5b expands into a broader range of psychological outcomes; as outlined in our conceptual framework, the feeling of groundedness should provide consumers with a sense of strength, stability, safety, and self-confidence. We test these outcomes in the context of using locally grown ingredients and also investigate alternative constructs to groundedness, such as self-authenticity, meaning in life, or sense of identity. Study 5a MethodWe randomly assigned 190 Austrian students (Mage = 22.5 years; 50.5% female; lab-based, for monetary compensation) to think about shopping at a supermarket or local farmers market. We then asked about their feelings of groundedness; happiness; and being connected to place, people, and past. Happiness was measured using three items (e.g., ""In the situation just described, how happy would you feel?""). Feelings of groundedness were measured using our three-item measure. Connection to place, people, and past were captured separately using three items each (e.g., ""Having been in the supermarket [to the farmers market] makes me feel connected to my physical/social/historic environment""). The order of the dependent measures (happiness, groundedness), as well as the order of the item blocks capturing connection to place, people, and past, were counterbalanced. Perceived quality and price were measured as control variables. Results and discussionChannel type has a significant effect on groundedness and happiness. Participants who thought about shopping at the farmers market reported feeling significantly more grounded (Mfarmersmarket = 4.66 vs. Msupermarket = 3.80; F( 1, 188) = 18.19, p < .001) and happier (Mfarmersmarket = 5.32 vs. Msupermarket = 4.87; F( 1, 188) = 7.94, p = .005). Consistent with our theorizing (H4), shopping at the farmers market leads to significantly higher perceived connection to place (Mfarmersmarket = 4.96 vs. Msupermarket = 4.03; F( 1,188) = 15.74, p < .001), people (Mfarmersmarket = 4.58 vs. Msupermarket = 3.45; F( 1, 188) = 24.60, p < .001), and past (Mfarmersmarket = 3.73 vs. Msupermarket = 2.52; F( 1, 188) = 25.47, p < .001). We also find support for serial mediation such that the effect of channel on happiness is mediated, in series, by connection to place, people, and past, and groundedness (for mediation results, see Web Appendix F). All effects remain robust when we enter quality and price as covariates.Study 5a thus supports our prediction that the feeling of groundedness increases consumers' subjective well-being (H3) while providing converging evidence for H1. Finally, the manipulation of distribution channel (H4) offers an actionable strategy for marketers to leverage groundedness.In our last study, we employ the context of locally grown ingredients to test a broader range of psychological outcomes of groundedness. We also test the explanatory value of groundedness against alternative constructs that are self-related, such as feelings of self-authenticity or meaning in life. Study 5b MethodThree hundred four students from a major European university completed Study 5b's online study for course credit. We excluded 12 participants for failing our reading check, leaving us with a final data set of 292 participants (Mage = 22.3 years; 69.5% female). Participants were asked to think about making apple pie on a Saturday; specifically, a pie with Boskoop apples—their favorite pie-making variety. In addition, they were told that these apples were from either an orchard only 12 kilometers from their home or an orchard 1,200 kilometers from their home. Participants then completed a short survey that measured five downstream psychological outcomes of groundedness using a five-item scale: ""I feel truly safe as a person,"" ""I experience a feeling of inner strength,"" ""I feel truly stable,"" ""I have a strong feeling of basic trust and confidence in myself,"" and ""I feel that nothing can stir me up"" (α = .89). Afterward, we measured feelings of groundedness using our three-item measure. Finally, participants completed four multi-item measures intended to capture alternative explanations (self-authenticity [e.g., ""I feel out of touch with the 'real me'""], meaning in life [e.g., ""I have a good sense of what makes my life meaningful""], self-identity [e.g., ""I have the feeling that I know who I am""], feeling of belonging [e.g., ""I have a feeling of belonging""]). Results and discussionParticipants who considered making apple pie with apples grown close to home scored significantly higher in terms of experiencing the related psychological downstream consequences than those using apples grown far away (Mlocal = 5.08, Mnonlocal = 4.61; F( 1, 290) = 12.22, p = .001). Thus, the apple pie made with local products boosted participants' personal feelings of strength, safety, and stability (for effects on the individual dependent variable items, see Web Appendix F). They also reported significantly stronger feelings of groundedness (Mlocal = 4.65, Mnonlocal = 4.06; F( 1, 290) = 15.20, p < .001). A mediation model ([20], Model 4) shows that the downstream consequences are mediated by feelings of groundedness (indirect effect = .27, CI95%: [.13,.44]). Importantly, the indirect effect via feelings of groundedness on the downstream consequences holds when we add, one at a time, each of the four alternative explanations as a rival mediator.Study 5b thus confirms positive psychological downstream effects of groundedness (H3) tested in the realm of local products. Products grown closer to the consumer—that is, products that are more strongly connected to one's place—make consumers feel not only more grounded but also stronger, safer, and more stable. General DiscussionIn this research, we have provided systematic evidence that products can provide consumers with feelings of groundedness by giving them a sense of connection to place, people, and past. We do so across nine studies (eight experiments and one survey), both online and in the lab, using different populations (business students, crowd workers on Amazon Mechanical Turk and Prolific, and members of commercial, representative panels) across two continents (total N > 3,000). We have tested our theory for robustness across a variety of product domains, including both disposable and durable consumer goods (food, care products, seasonal products, and tableware), using real brands to strengthen external validity as well as highly controlled stimuli for internal validity. We have provided process evidence via mediation, moderation, and moderated mediation. Theoretical ImplicationsThis work introduces feelings of groundedness to the marketing literature by identifying these feelings as an important construct for marketing research and systematically examining it as a driver of consumer behavior. While references to groundedness and related constructs can be found in philosophy (e.g., [47]), different domains of psychology (e.g., [29]), and psychotherapy ([31]), the concept of groundedness is new to experimental research in marketing, consumer behavior, and mainstream psychology. Existing research in consumer culture theory has given passing treatment to concepts such as ""rooted connections"" ([42]) and has definitely been inspirational to this work. However, it has neither discussed nor empirically explored the full concept of groundedness with its antecedents, proxies, boundary conditions, and consequences, which we have aimed to do here.We also contribute to the growing literature on consumer well-being. [47], p. 43) proposes that ""every human being needs to have multiple roots. It is necessary for him to draw well-nigh the whole of his moral, intellectual, and spiritual life by way of the environment of which he forms a natural part."" Our work indeed shows that groundedness is related to happiness and a sense of strength, stability, and safety; thus, we propose groundedness as a novel antecedent of these outcomes.We also theorized about three sources of feelings of groundedness: connections to place, people, and past. Although the three sources are often empirically intertwined, we show that they are theoretically distinct and powerful in fueling consumers' feelings of groundedness. Our analysis further provides rich insight on the nature of these connections by showing that the extent to which products provide feelings of groundedness is a graded function of closeness. That is, a product provides stronger feelings of groundedness when the product's place, people, or past is closer to the consumer. Finally, by identifying the role of groundedness and its sources, we offer an overarching theoretical explanation for major current consumer trends, such as buying local products (connected to place), produced by people we relate to (connected to people), and according to traditional production methods (connected to the past). Marketing ImplicationsFeelings of groundedness are worthy of managers' attention because these feelings have important downstream consequences as shown across our studies. In particular, feelings of groundedness impact consumers' brand preference and WTP. In Study 1, for example, consumers were willing to pay a price premium of about 60% for the product that provided more groundedness.Our work also provides actionable implications for product and brand management: we give concrete approaches regarding how firms can elicit groundedness by showing consumers their product's connection to place, people, and past. For example, our results in Studies 1, 2a, and 2b show how presenting a product as artisanal or highlighting the local origin of a product can provide feelings of groundedness. In Studies 3a and 3b, we have shown that managers can utilize other marketing-mix elements such as product design or retail assortment and configure them (e.g., as more traditional instead of modern) to provide a stronger connection to the past. Similarly, Study 5a shows that a marketer's choice of distribution channel (e.g., farmers market) has an impact on feelings of groundedness.In terms of customer targeting, we have pointed out when and for whom groundedness is more important. In particular, we have shown that traditional (vs. innovative) products benefit from situational differences in the need for groundedness (Study 3b). On the level of individual differences, in Study 3a, only consumers with a high chronic need to connect to the past preferred the more traditional cutlery design. Our representative survey (Study 4) further showed a higher need for groundedness among consumers who are particularly affected by large global trends or major disruptive events. These global trends (e.g., digitization, urbanization) and major life events (e.g., the COVID-19 pandemic) make it harder for consumers to feel connected to people, place, and past. From a groundedness perspective, it is not surprising that during the safety- and stability-threatening COVID-19 pandemic, customers returned to the familiar grocery brands consumed with their families while growing up ([ 7]). There are probably multiple drivers for this behavior, but it is likely that consumers chose these products, at least in part, because of the connections to place, people, and past—and thus feeling of groundedness—they provide. Limitations and Future ResearchThis is the first series of experimental studies investigating feelings of groundedness. As such, many questions remain for future research. With regard to antecedents, for example, we have focused on products as means for consumers to experience feelings of groundedness. However, anecdotal evidence suggests that there are other ways for consumers to feel more connected to place, people, and past and, consequently, more grounded: for example, through services such as genealogy websites, cooking classes, lectures on local history, or yoga and meditation classes providing ""grounding"" exercises.The scope of Study 4 has allowed us to identify an initial set of indicators for who has a higher need for groundedness and why, but it is clear there will be additional consumer characteristics and lifestyle variables helpful to marketers in identifying relevant customer segments. For example, people who travel frequently for work and have little chance to connect to their current physical environment may seize opportunities to (re)-connect to place—such as through a local craft beer—to feel more grounded. Likewise, pandemics such as COVID-19 are not the only type of events that can shake a person's foundation. Stressful life events such as separation or loss, starting a new job, or moving homes may cause a higher need to feel grounded. Similarly, the need for groundedness may be subject to seasonal variations. Preliminary insights from our own qualitative explorations suggest that individuals' need for groundedness may be particularly high during the holiday season and other festive occasions, such as Christmas, Thanksgiving, Ramadan, and one's own birthday. Apart from that, interestingly, the need for groundedness appears to be higher during the colder seasons. We believe a more thorough testing of these hypotheses seems promising and would likely have important implications. If the initial signals are correct, for example, studies of scanner or panel data should reveal variations in the demand for products that connect to place, people, and past across the year.Finally, we have only begun to examine boundary conditions. For example, it seems possible that in some situations strong roots not only provide strength and stability but could also constrain movement, thus giving consumers the feeling of being ""stuck"" and unable to escape their roots. Imagine growing up on a farm, surrounded by one's family, and doing things day after day in the same way they have traditionally been done by previous generations. A person in this situation will likely feel grounded but might also feel more motivated to break free, move away, or challenge the status quo. If such is the case, too much groundedness might even backfire. Future research might thus enrich the present investigation by focusing on potential downsides of groundedness. ConclusionThis research introduced feelings of groundedness as a relevant construct for marketing research and consumer behavior. We have demonstrated its importance to marketers by documenting that it increases product attractiveness and that it can be manipulated through a variety of marketing-mix strategies and used for targeting consumer segments prone to a lack of groundedness. We also have shown that groundedness is important to consumer well-being, pointing to important consumer welfare and policy implications. We expect that the importance of this topic to consumers and marketers will only increase as digitization, urbanization, and global migration continue to challenge consumers' connections to place, people, and past. " 12,Consumer Self-Control and the Biological Sciences: Implications for Marketing Stakeholders," The authors argue that appreciation of the biological underpinnings of human behavior can alter the beliefs and actions of multiple marketing stakeholders in ways that have immense welfare implications. However, a biological perspective often deviates from the lay perspective. The realization of improved welfare depends in part on narrowing this gap. The authors review biological evidence on self-control and report ten empirical studies that examine lay response to biological characterizations of self-control. The authors contrast lay response with scientific understanding and then offer implications of biology—as well as the gap between the scientific and lay perspectives—for policy makers, firms, consumers, marketing educators, and scholars. The authors also identify opportunities for future research. They conclude that marketing scholars can and should play an active role in narrowing the gap between the scientific and lay perspectives in the service of both theory development and human welfare.","The hope is that when it comes to dealing with humans whose behaviors are among our worst and most damaging, words like ""evil"" and ""soul"" will be as irrelevant as when considering a car with faulty brakes, that they will be as rarely spoken in a courtroom as in an auto repair shop....When a car is being dysfunctional and dangerous and we take it to a mechanic, this is not a dualistic situation where (a) if the mechanic discovers some broken widget causing the problem, we have a mechanistic explanation, but (b) if the mechanic can't find anything wrong, we're dealing with an evil car. ([82], p. 611)One general message that should emerge from these discoveries is tolerance for others—and for ourselves. Rather than blaming other people and ourselves for being depressed, slow to learn or overweight, we should recognize and respect the huge impact of genetics on individual differences. ([73], p. 91)Society's understanding of human ills is constantly evolving. Tuberculosis is no longer viewed as a lifestyle disease; epilepsy is no longer viewed as evidence of demonic possession. Today, we understand autism as a disruption of circuitry in the ""social brain,"" but three generations ago, a prominent child psychologist claimed that autism resulted from a mother's withholding of affection from her unwanted child. We have slowly experienced a shift in attitudes toward depression and posttraumatic stress disorder, conditions that previously were considered character issues but now are understood as biological syndromes with clear neural etiologies. In each of these instances, a nonbiological account was replaced with a biological account. We argue that biology can similarly advance marketing's contribution to human welfare if it is included as a complement to traditional psychological, anthropological, and economic perspectives on consumption, particularly with respect to the vital topic of self-control. As a behavior, self-control is fundamental to human welfare; as a trait, self-control is central to our evaluations of ourselves and others; as a principle, self-control is woven into the fabric of our legal, political, and religious institutions. In many cases, self-defeating consumption reflects a self-control failure, sometimes abetted by marketing practice.Our argument takes both sides of the debate coin ([53]): to our research peers, we engage in advocacy; to others, we refute the welfare-defeating policies that stem from indifference to, or rejection of, biological causation. To do so, we consider two biological domains that have produced a tsunami of findings in very recent years: neuroscience and genetics. We argue, however, that biological insights will not translate directly into improved welfare if those insights fail to make an impression on marketing's many stakeholders. We further argue that the road to welfare-enhancing policies will be rocky if the lay public is resistant to the implications of biology. Our task in this regard is to understand laypeople's existing beliefs about biological causation and, moreover, to gauge how those beliefs can be shaped by findings from the biological sciences.Our presentation is organized accordingly. The first section describes relevant biological findings. The second section summarizes a set of studies that investigate lay response to these biological findings and, in so doing, illustrates the gap between science and lay beliefs. The third section offers implications of the first two sections for marketing's many stakeholders ([54]) and identifies opportunities for future research. A Biological PerspectiveIn this section, we discuss biological causation first in terms of neuroscience and then in terms of genetics. Definitive accounts are aspirational, but the current state of the art suffices. When appropriate, we speculate about likely departures from lay beliefs in anticipation of our subsequent empirical investigation. NeuroscienceWe focus on three broad realms of human behavior in which the gap between lay beliefs and neuroscience is consequential for human welfare: decision making, compulsion, and impulsivity. Decision makingIn one sense, the proposition that decision making involves the biological brain is uncontroversial. Neural activity underlies every memory we retrieve and every inference we draw, and disorders that impede mental activity (e.g., Alzheimer's) are understood to be biological conditions. However, both prior research and the studies we present in the next section reveal laypeople's strong dualistic[ 6] tendency to view reasoning and decision making as unconstrained by biology. To address the neuroscience of decision making, we draw on the bottom-up relationship between the limbic system (including the amygdala) and prefrontal cortex (PFC), colloquially known as the seats of emotion and executive control, respectively.Research in this vein suggests that pure reasoning in everyday life is rare; that is, decision making so routinely involves emotion that the distinction between thought and feeling is a false dichotomy. This assertion can be traced to [19] somatic marker hypothesis. Damasio begins with the uncontroversial premises that emotions are bodily responses to a situation and, furthermore, that associations are formed between features of the situation and those bodily responses. Similar emotions will arise if one reexperiences the situation but also if one merely anticipates the situation ([ 4]). One key feature of Damasio's hypothesis is that these bodily responses can be mentally represented in the brain ([17]), thereby bypassing the body and enabling speedier responses. Put differently, internal representations of those bodily responses encode what a particular outcome would feel like. This information is relayed to the ventromedial PFC (vmPFC), which integrates the emotional signals with response options. A second key feature of the hypothesis is that a decision may be informed by emotional inputs that do not rise to the level of conscious awareness. Regardless, the net positive and negative somatic signals will either determine the decision or bias it.The somatic marker hypothesis explains why risk assessments may not align with objective cost–benefit calculations ([51]). The hypothesis also provides a process explanation for some system 1 judgments[ 7] ([40]).This biological account of decision making is pertinent to our argument in two ways. First, because emotion can play a role without ever entering one's consciousness, the pervasive influence of emotion on decision making is likely not apparent to the lay mind, so laypeople may overestimate their capacity to rise above the fray and make reasoned judgments as autonomous beings. Lay and scientific beliefs are especially likely to clash in the case of moral judgment. Consider ""moral dumbfounding,"" in which deeply held moral beliefs are based on an intellectually hollow intuition or feeling of moral rectitude ([32]). This phenomenon comports with [19] own clinical evidence that the extent to which people make socially favored (vs. purely utilitarian) judgments depends on their vmPFC. Patients with damaged vmPFCs make poor personal, social, and moral judgments not because they do not comprehend the objective implications of their judgments but because they are unable to feel the implications of those judgments. We anticipate that laypeople would be especially surprised that the moral judgments that define us are biologically rooted in the tangible brain rather than in the intangible mind.Second, a biological account of decision making is consistent with the view that moral/social ""deviance"" exists on a continuum. Over the past two generations, psychiatry has bent to the logic of biological causation and now understands that psychopaths suffer from abnormalities in the limbic system and in the connection between the limbic system and the frontal cortex, resulting in severely reduced empathy and interpersonal connections ([41]). For those of us closer to the mean, our social judgments still vary with the strength of the connection between the limbic system and PFC, as well as with the relative roles played by the utilitarian dorsolateral PFC and the feelings-oriented vmPFC. The difference between a psychopath and an unempathetic person may be a matter of degree rather than a matter of kind ([ 8]). Regarding our subsequent argument regarding human welfare, it is also important to note that structural deficiencies in the brain may be caused by multiple factors. Specifically, structural brain changes can be caused not only by disease and accident but also by less sensational environmental factors that are pervasive and distributed unequally across the population (e.g., poverty). CompulsionMarketing has been implicated as complicit in self-defeating acts of compulsive consumption, including substance abuse ([57]) and overeating ([63]). We consider the biological roots of these behaviors.[ 8]Addiction is a morbidity that neuroscience has come to understand with some certitude (see [49]). We suspect that the public now feels some sympathy for the problem and a willingness to accept its biological causality in the colloquial sense that drugs can ""hijack the brain."" The lay public may also view the lure of a drug in terms of the intense pleasure it evokes. By contrast, neuroscientists view addiction as a malfunction of the brain's reward circuitry, with a ""reward"" defined simply as an object or event that induces approach behavior. Different substances may act on different brain loci, but they all increase dopamine concentrations in the striatum. Consumption also creates a conditioned response to the environmental cues that predict dopamine release. Subsequent exposure to these cues induces intense craving ([ 6]). Because these cues are unlikely to be eliminated from the drug user's life, addiction is a chronic disease, as the potential for relapse lurks forever. Thus, relapse is viewed by many experts as a biological syndrome rather than a failure of willpower. The trap of addiction is further exemplified by evidence that long-term substance abuse can effect changes in the brain's reward system, including a possible reduction in dopamine sensitivity. Such an alteration requires more intense consumption to attain the same hedonic outcome ([41]).Although drug addiction is increasingly receiving lay sympathy, the overconsumption of food is less likely to be attributed by laypeople to a hijacking of the brain. The causes of obesity are varied and complex, but we now have abundant insights into its biological causation and the futility of mere willpower ([10]). Notably, a biological explanation akin to substance abuse is emerging in the form of a ""food addiction"" model of obesity. The drug addiction analogy works on multiple levels: food consumption has a compulsive element, consumption evokes similar changes in the reward circuitry of the brain ([12]), and the cessation of consumption triggers withdrawal symptoms ([83]). In terms of a crude stop-go model, evidence suggests that overt consumption is determined by the relative signaling strength of competing clusters of neurons, such that consumption is triggered when the ""eat"" (go) cluster fires more strongly than the ""stop"" cluster. In a further nod to addiction, the process appears to stimulate the brain's opioid system ([99]). The reward system of obese people tends to be less responsive to dopamine and to have a lower density of dopamine receptors; thus, as with drug addiction, even greater consumption is required to attain sustained pleasure ([41]). In summary, biology's conceptualization of obesity is a far cry from society's character-related attributions that revolve around gluttony.It has also been argued that obese individuals are victims of a second vicious cycle driven by the prevalent stigma that accompanies being overweight (and in which marketing is again complicit; [30]). Stigma leads to shame and stress. Stress results in higher levels of cortisol, which in turn leads directly to fat deposition and indirectly to a more sensitized food reward system and greater food consumption ([92]). ImpulsivityOur primary emphasis in this research concerns deficits in self-control that are neither temporally bound nor context-specific. Rather, we focus on self-control as an enduring trait that is shaped early (even prenatally) in life.The granular biological story is intricate, but the bird's-eye view again invokes the hijacking metaphor and the relationship between the amygdala and PFC. Unlike emotion-based decision making, however, trait self-control involves a top-down relationship between the PFC and amygdala. Deficits in trait self-control involve a neural system that is perverted by excess glucocorticoid production ([70]). Glucocorticoid production is an otherwise adaptive response to situational danger; in modern society, however, stress is often chronic, as when it derives from the duress of poverty, physical or emotional abuse, exposure to violence, parental separation, or other adversities ([31]; [43]; [67]). Such sustained psychological stress produces a continual oversupply of glucocorticoids, with damaging outcomes that take root in childhood and persist over the lifetime ([ 7]; [24]; [52]).The neurological story and its consequences are neatly captured by [82]: childhood adversity in the form of poverty leads to higher basal glucocorticoid levels and/or a more reactive glucocorticoid stress response; thinner frontal cortex with a lower metabolism and less excitable synapses; and poorer frontal functioning in the domains of working memory, emotion regulation, impulse control, and executive decision making. Childhood adversity can atrophy and blunt the functioning of the hippocampus and frontal cortex ([34]) while stimulating the amygdala to enlarge and develop more excitable synapses ([102]). Consequently, childhood adversity empowers the amygdala to inhibit the frontal cortex, which ordinarily should gain the ability to inhibit the amygdala during adolescence. Childhood adversity also damages the dopamine system, leaving the adult more vulnerable to drug and alcohol addiction ([88]). Such damage can also lead to depression, learned helplessness, blunted empathy, and lower prosociality. Thus, it again may be said that the executive control system of individuals with low self-control has been hijacked by their more primitive limbic system ([14]).Our previous discussion of decision making and compulsive consumption makes it clear that pure reasoning (i.e., emotion-free cognition) not only is rare but also provides little help to those who wish to escape from the acute problems of addiction and obesity. Problematic decision making and compulsive consumption can be due to either brain injury or counterproductive alterations in the brain's reward system in response to consumption. Our major assertion here about impulsivity is that environmental conditions also can adversely influence the brain subtly, but with cumulative effects. We speculate that because the biological account of impulsivity imputes subtle and cumulative effects, it may be elusive to the lay mind. As a result, the lay public may be less sympathetic toward (and surely less informed about) the initial and often irreversible decisions that lead to addiction in the first place, despite lay sympathy regarding the difficulty of escaping addiction. In turn, misconceptions about the initial behavior could translate into a lack of sympathy toward the drug abuser. However, as we shall contend later, it is this biological explanation for why someone would initially travel down a destructive path (i.e., the biological roots of impulsivity) that can provide a basis for public policy intervention. GeneticsA complete account of biological causation requires a discussion of genetics, inasmuch as the role of the genome in ""psychological"" phenomena is now known to be sizable. When genes influence thoughts and behaviors, the brain may be the mediator, but the influence of genes can be appreciated in the absence of mediation, including in the applications of central concern here. Marketing has recognized the relevance of neuroscience ([72]), but the role of heritability has largely been ignored ([20]; [86]). The trajectory of behavioral genetics research is steep, but even in its present state, the field's implications are profound. We highlight the following key points to inform stakeholder implications. Traits are highly heritableAcross a diverse set of traits, including psychological traits, genes have been shown to account for more than 50% of the variance, with the remaining variance being largely unsystematic ([44]; [75]; [93]). More pertinent to our consumption theme, weight is highly heritable.[ 9] Across twin studies and adoption studies, the heritability of weight is estimated to be about 70% ([73], p. 29). The mechanisms by which genes influence weight are still under investigation ([50]), but evidence indicates that body mass index (BMI) is a function of the body's hereditary predisposition to gain fat when food is plentiful. For example, research shows that genes associated with higher BMI (e.g., the FTO gene; [16]) result in greater responsiveness to food cues and decreased feelings of postconsumption satiety. Thus, individuals with a genetic tendency toward a high BMI will have a greater propensity to gain weight and will experience more difficulty losing it.However, despite these large and robust genetic influences, the lay public is unevenly calibrated regarding its understanding of the genetic contribution to different traits. For example, people vastly underestimate the heritability of weight and school achievement but appear relatively well-attuned to the significant heritability of height and schizophrenia ([73]). Traits are mostly polygenicA trait is polygenic if it is controlled by many different genes. A major development in genetic science is the ability to use a person's genetic code (reflected in an individual polygenic score) to predict from birth that person's propensity to exhibit particular dispositions or outcomes. For example, [87] used polygenic scores to predict academic motivation and the Big 5 personality traits. Moreover, the polygenic nature of most traits suggests that genetically influenced traits exist along a continuum. In contrast to outdated medical models of mental disorders, traits such as schizophrenia and autism are now recognized not as categorical syndromes but as existing along a spectrum. The same logic applies to various personality traits (e.g., extraversion), physical states (e.g., BMI), and behavioral tendencies (e.g., addiction); that is, everyone possesses a polygenic score that places them somewhere along the spectrum, and ""normal"" is defined as a particular range of a characteristic. Genes are not destinyDespite the large share of variance explained by genes, the unexplained variance suggests that it is possible, albeit difficult, to swim against the genetic tide (as in the case of weight). It is also important to appreciate the omnipresent gene–environment interactions. Genes and environment combine to produce neurological outcomes. An important example is childhood adversity. The same adverse event does not impact every child equally; some children are genetically more resilient than others ([ 5]). This observation is especially important from a policy perspective if individual polygenic scores can identify the most vulnerable individuals and target intervention accordingly. Using Biology to Enrich Social ScienceDrawing on neuroscience and genetics, we have tried to reinforce our opening observation that understanding can improve when a nonbiological explanation is replaced by a biological explanation. We conclude this section with an illustration of how biology can add explanatory depth to social science. [78] showed how risk factors associated with family structure lead to materialism and compulsive consumption and, further, how the effect of these risk factors can be mediated by the stress they place on the individual. Subsequent research reinforced the importance of stress on consumer behavior and well-being ([62]; [81]). The biology literature reviewed previously adds depth by clarifying the underlying neurological mechanism.Although enriching a behavioral phenomenon with biology may inspire confidence among social scientists, our presentation thus far is mute regarding whether a biological explanation of social phenomena can alter lay opinion. Along this line, [105] found that social risk factors alone did little to alter people's beliefs that an individual subjected to those risk factors had control over undesirable outcomes that arose subsequently. Consequently, public policies and marketing practices that build on findings such as [78] may not engender public acceptance.[10] Given the importance of public support for the success of policy and practice, we next examine whether biological evidence can alter public attitudes. When Lay Beliefs Meet the Biological SciencesResearch on lay reactions to neural processes, neural structures, and genetics is sparse. [105] did find that when a consumer's lack of self-control over food consumption was described as attributable to the ""short-circuiting"" of a brain center via a firm's development and sale of ""hyperpalatable foods,"" participants perceived that the consumer had less personal control over their food consumption. However, it is unclear that laypeople would be similarly forgiving in the absence of a malign external agent or in the presence of different forms of biological causation.To address these issues and inform policy and marketing practice, we conducted ten studies that examined lay reactions to various biological models (specifically, neural processes, neural structures, and genetics) of self-control. The studies measured participants' perceptions of either ( 1) a protagonist's control over a lapse of self-control or ( 2) their own vulnerability to the same lapse of self-control. The former assesses whether the perceiver has sympathy for the protagonist's plight; the latter assesses whether the perceiver can empathize with the protagonist. Both measures inform public policy because greater sympathy and empathy would presumably lead to greater willingness to support intervention. To simplify the exposition, we initially spotlight results regarding moral transgressions (e.g., shoplifting) among individuals who possess a high belief in free will (the dominant segment of the U.S. population). Table 1 provides a summary of our findings, Web Appendix A contains a summary table of the experimental design and statistical results of the studies, and Web Appendix B contains the full details of each study. We describe the most important takeaways in narrative form.GraphTable 1. Summary of Findings. Lay Conceptions Diverge from BiologyOur initial studies (Experiments 1a and 1b) presented participants with a veridical but simplified biological model that described self-control as determined by the relative strength of two competing neural pathways (i.e., ""go"" vs. ""stop""). Participants were asked to consider a protagonist who failed to resist a temptation and to choose one of three visual depictions that best fit their own interpretation of the scenario. The depictions characterized the neural processes as ( 1) correlated with the protagonist's willpower failure but not causal, ( 2) the consequence of willpower failure, or ( 3) the cause of willpower failure. Participants then rated their own likelihood of succumbing to the temptation if the same neural processes took place in their own brain.We found that half the participants attributed self-control failure to the causal influence of neural processes (i.e., the third depiction). Regardless of their choice of the visual depiction, however, participants assessed their own likelihood of succumbing to the temptation as low. Participants who interpreted the neural model as causal rated their own likelihood of transgressing at less than 20%, which was statistically indistinguishable from the self-assessments provided by participants who interpreted the neural model as noncausal.Experiment 1a also showed that appreciation of biology is behavior-dependent. When the transgression was amoral (e.g., succumbing to a tempting food), biological causation was embraced more frequently and self-vulnerability was more willingly acknowledged. Together, these results suggest that participants viewed themselves as unconstrained by biology, especially in the case of a moral transgression. Lay Belief Is Rooted in an Intangible SelfIn Experiment 1b and all subsequent studies in which participants were asked to estimate the likelihood that they would fall victim to the same self-control failure as the protagonist, we followed up by asking participants to indicate the root of their superior ability to exert self-control. Participants chose from four options: the tangible brain, other tangible body parts, the intangible mind, or the intangible soul. Across Experiments 1b−3d, most participants chose the intangible mind or soul; in Experiment 1b, 82.0% of the participants did so. These results provide direct evidence of the dualistic nature of lay beliefs regarding self-control.Experiment 2 followed by directly examining people's view of the roots of self-control. For comparison, it also explored people's views of the roots of intelligence, integrity, and empathy. Participants were asked to indicate the locus of the differences in these traits across the human population by choosing from the tangible brain, other tangible body parts, the intangible mind, or the intangible soul. Results showed that all traits were viewed as predominantly intangible, but with intelligence deemed less intangible (67.4% of participants) than self-control (73.7%), integrity (84.8%), or empathy (81.9%). Notably, more participants perceived the intangible soul to be the locus of integrity and empathy (24.6% and 26.6%, respectively) than the locus of intelligence or self-control (3.9% and 5.4%, respectively). Thus, the morality-tinged traits of integrity and empathy were viewed less secularly than self-control. The strong inclination to characterize each trait as intangible (ranging between 67.4% and 81.9%) is consistent with lay acceptance of mind–body dualism ([ 9]) but contrasts with evidence that human traits have a strong genetic basis ([73]). Lay Belief Is MalleableMany neuroscientific findings consist of a correlation between a mental phenomenon and brain activity. Because people view their virtues as intangible (see Experiments 1b and 2), they may view the mere presence of neural activity as insufficient evidence of biological causation. In contrast to mere brain activity, we speculated that physical alterations to brain structures would be less easily dismissed.In Experiments 3a−3d, we described a self-control failure as the result of structural damage in the brain. In Experiment 3a, participants were told of a relationship between childhood stress and a later lack of self-control. We manipulated biological causation by either presenting or withholding scientific physiological evidence (i.e., ""Stress leads to overproduction of glucocorticoid hormones in the developing brain, which leads to a thinner and less connected PFC, the part of the brain responsible for self-control""). Participants were then asked to consider a protagonist who was described as having suffered from childhood stress and who had failed to resist the temptation to shoplift. Some participants were asked to assess the protagonist's control over the behavior, with results showing that perceived control significantly declined in the presence of the biological model. Other participants were asked to assess the likelihood that they would succumb to the same temptation under the same neural condition as the protagonist. Results here showed that the biological model had no effect on perceived self-vulnerability. In Experiment 3b, the effect of biology on self-vulnerability remained nonsignificant, even though the verbal description of brain damage was accompanied by a visual image of modest brain damage. Experiment 3d likewise found a null effect of biology when it was described in genetic rather than neurological terms. Only in Experiment 3c, when the verbal description was accompanied by an image of extensive brain damage and an image of a healthy brain for comparison, did participants perceive that they themselves would be vulnerable to biological causation.Thus, mere references to neural damage lowered participants' perception of the protagonist's personal control, but participants acknowledged their own vulnerability only when they imagined suffering the same extensive brain damage as the protagonist. As in Experiment 1b, a majority of participants (ranging from 78.5% to 81.0% across Experiments 3a−3d) regarded their superior ability to resist the temptation as due to their intangible qualities. Empathy Lags SympathyThe preceding results also document a divergence between the perceived control and perceived self-vulnerability measures. That is, the amount of control perceived in another (an indicator of sympathy) does not correspond to an assessment of one's own vulnerability (an indicator of empathy). This distinction is important because, as we subsequently discuss, support for treatment and prevention policies should rise when the transgressor is perceived to lack control and fall when people are unwilling to recognize their own vulnerability. Our results suggest that perception of personal discretion is more malleable when the judgment involves others than when it involves the self. This tendency may be due in part to an individual's belief that they possess more free will than others ([76]) and/or confidence in their superior intangible self. Deliberation Heightens Belief in Personal ControlExperiment 4a presented participants with the competing-pathways model used in Experiments 1a and 1b and varied whether the decision to succumb to temptation was made within seconds or after some deliberation. Results showed that deliberation neutralized participants' perceptions of the causal role of biology. This neutralizing effect was replicated when the biological model was based on genetics (Experiment 4b). These findings reveal the lay belief that conscious deliberation provides a route for the intangible self to override a biological process; these findings also corroborate prior research showing that lay perceptions of free will and responsibility are not undermined by neural evidence that decisions are made unconsciously, as long as the decisions are based on the decision maker's own reasons ([65]; see also [79]). Lay Beliefs Influence Policy PreferencesA key element of our argument is that the implications of biology for marketing's stakeholders are dependent on lay beliefs about the role of biology. To illustrate, we conducted a final study (Experiment 5) that examined the policy consequences of neuroscience. Participants were assigned to one of three conditions (no model, neuroprocess model, or brain-structure model). All participants read, ""Research has established that excessive stress during childhood strongly inhibits a person's later ability to exhibit willpower and resist temptation."" Those in the no-model condition received no further information; those in the neuroprocess model condition were additionally presented with the competing-pathways description used in Experiments 1a, 1b, and 4a; and those in the brain-structure model condition were additionally presented with the brain damage description and visual brain images used in Experiment 3c. Participants were then asked to rank their affinity toward six public policies aimed at addressing problems associated with a lack of self-control. Three policies aligned with the neurological and developmental nature of self-control described in the biology literature reviewed previously. Results showed that support for science-based policies (e.g., funding social programs to prevent excessive stress in childhood) increased when biology was portrayed in terms of structural damage to the brain but not when portrayed in terms of neural processes. Individual Differences Moderate Acceptance of BiologyOur results thus far have focused on individuals who hold a high belief in free will. We now turn to how this belief moderates the effect of biological evidence on lay beliefs of self-control (for the measures, see Web Appendix D). Across studies, we found that, in the face of biological evidence of self-control, people with a lower belief in free will perceived themselves to be relatively more vulnerable to committing a transgression (Experiments 3b and 3d) and were less influenced by deliberation when assessing the control possessed by others (Experiments 4a and 4b). Moreover, in terms of policy preferences, people with a lower belief in free will were uniformly inclined to endorse policies consistent with neuroscience (Experiment 5; the same pattern of results was obtained when political conservatism was substituted for belief in free will as the individual-difference variable).We also found a consistent effect of religiosity, as measured by self-reported attitudes and behaviors regarding religious practices (for the measures, see Web Appendix D). We calculated a perceived intangibility score based on participants' responses to the perceived locus questions (Experiments 1b−3d). In Experiment 2's investigation of traits, we found that religiosity was significantly associated with the perceived intangibility of integrity and empathy but not intelligence or self-control. Across Experiments 1b and 3a−3d, religiosity was significantly associated with the perceived intangibility of the ability to refrain from temptation despite sharing the same neural processes or the same damaged neural structure as the protagonist. DiscussionLay beliefs influence our understanding of the natural world. The teleological view of evolution, for example, has been characterized as a fundamental feature of the human psyche that thwarts the teaching of natural selection ([27]). Closer to home, a lack of scientific understanding can give rise to false beliefs that thwart the diffusion of welfare-enhancing production technologies such as food irradiation and genetic modification ([104]). In the present case of self-control, the results of our studies tell a story of malleable resistance to biological causation. On the one hand, resistance is sizable and entrenched in the lay belief of mind–body dualism. On the other hand, laypeople's perceptions of others' control and their own vulnerability vary with the portrayal of biological causation, the nature of the transgression, the amount of deliberation by the transgressor, and individual differences across the population. These factors explain why dualism persists: biological causation is rarely salient, many transgressions have a moral tone, and a high belief in free will is pervasive. Thus, it is unlikely that society will readily adopt the neuroscientists' perspective that our brains are best viewed as ""complex relay points for innumerable inputs, rather than command centers endowed with true self-determination"" ([39], p. 5). Nonetheless, the malleability aspect of our findings is informative and instrumental to marketing's stakeholders, due in part to marketing's ability to shape people's beliefs about biology. Thus, we emphasize malleability in the following discussion of the implications of biology and lay understanding. Implications and Future ResearchWe endorse calls for boundary-spanning in marketing research ([60]). This approach has many virtues—including, notably, that marketing can influence its external stakeholders while opening new opportunities for the discipline ([54]). The implications developed in this section sit at the intersection of biology and lay beliefs about biology and inform policy makers, firms, consumers, marketing educators, and academics of different disciplines. Table 2 highlights the key implications and future research questions, to which we turn next.GraphTable 2. Implications and Sample Research Questions for Marketing Stakeholders. Policy MakersAmong marketing's many stakeholders, a principal audience for biology is policy makers. Our preceding discussion indicates that the foremost policy application is the treatment of morbidities, which we discuss first. Then, we note policy implications as they apply to jurisprudence and privacy. MorbidityPolicy makers exhibit uneven recognition of the role biology plays in self-control. At one end, we sense a growing and relatively uncontroversial acceptance of the role played by biology in imprudent adolescent behavior. As [71] argue, the immature PFCs of teens that prompt risky consumption serve as a basis for regulating such activity. More recently, research has begun to examine the neural effects of vaping on the teenage brain—especially with regards to memory, learning, focus, and impulse control ([33])—which would carry evident regulatory implications if confirmed. In addition, policy makers and social-welfare organizations should include evidence from biology in public service announcements that warn against unwise consumption.Beyond this protected class of consumers, some specific domains of consumption should also be on policy makers' radar due to their sizable impact on consumer welfare.[11] Our empirical results suggest that acceptance of the causal role of biology in obesity is tentative (see Experiments 1a, 4a, and 4b). Insensitivity to biology in alcohol and substance abuse is reflected in halting utilization of effective pharmaceutical therapies ([29]). For example, in the case of alcohol addiction, 12-step programs that claim to address underlying ""character defects"" are held in high esteem, despite their uncertain efficacy ([29]). In the case of opioid addiction, underutilization has been attributed in part to skepticism among the lay public, therapists, and the judiciary regarding the appropriateness of a pharmaceutical intervention to address the ""underlying"" cause of the problem. Some stakeholders maintain that the use of antiaddiction drugs serves only to replace one disorder with another, so they favor abstinence over pharmaceutical intervention, despite the proven success of the latter ([66]; [80]). A biological perspective should shift perceptions of the acceptability and legitimacy of competing interventions.At the broadest level, consider the case of trait self-control, which exhibits the least appreciation of biology. One analysis holds that personal decisions are the leading cause of premature death and that ""individuals have a great deal of control over their own mortality"" ([42], p. 1345). The empirical result reinforces the tragedy of poor self-control, but the conclusion regarding control fails to consider the roots of those ill-advised personal decisions. We have argued that trait self-control serves as an antecedent to acute manifestations of self-control failure. We argue here that trait self-control has the broadest policy implications.Biology shows that trait self-control can be diminished by childhood adversity, and social science shows that self-control in early childhood serves as a predictor of adult health, wealth, and criminality ([59]). More specifically, childhood adversity and the unhealthy lifestyles that may ensue have been associated with a range of morbidities that in turn lead to seemingly intractable economic, racial, and ethnic disparities in welfare and opportunities that are measurable at the level of gross domestic product ([85]). The mediating influence of neurological development is reflected in evidence that adverse childhood experiences hinder the developing brain, as previously discussed in the biology section.Policy makers at the highest levels of the executive and legislative branches devise policies that influence the developmental trajectory of trait self-control (e.g., via social support). It is likely that many of these high-level decision makers share the lay public's beliefs about personal control. Thus, biology can enlighten policy directly if policy makers gain a deeper understanding of the biological roots of various morbidities. Such enlightenment would illuminate both the need for costly interventions and the nature of those interventions. However, policy makers are moored to the voting public. Changes in policy can be accelerated by public support and can be thwarted by its absence. Beliefs regarding the appropriateness of government action have been associated with lay beliefs about personal control and self-sufficiency ([26]; [103]). Lay support for intervention should rise with lay acceptance of biological causation.Toward this same end, public confidence would be enhanced by scientific evidence of successful intervention. For example, a PFC already damaged by adverse childhood experiences is not beyond repair ([21]). It is likely that broad-based prevention policies have higher up-front costs than treatment policies, but the public should also be encouraged by the compelling relationship between the earliness of intervention and the return on investment ([36]; [45]; see also [37]).We ultimately argue that marketers can play a large role in educating the lay public about biological causation. In so doing, marketers can tackle questions that are amenable to research. For example, whereas economic research may examine the returns on prevention versus remediation, marketers can explore the public's attitude toward each, both a priori and following exposure to biology-based explanations. In the face of continued public resistance, future research could examine tactics that nudge the public toward policies that would otherwise raise objections related to personal freedom and thereby overcome the political divide observed in Experiment 5's probe of policy preferences ([91]). JurisprudenceThe increasing prominence of bioscience has prompted concern over the demise of personal responsibility, with transgressors asserting, ""My brain made me do it."" We expect that, in pursuit of social order, society will retain its view of personal responsibility ([28]). However, it is worth contemplating whether there will be a shift in the ""reasonable person"" criterion applied in the law, which currently treats physical and mental limitations differently. If mental limitations come to be viewed as documentable physical limitations, forbearance could climb.Beyond the realm of personal responsibility, the rise of bioscience will also lead to ethical and regulatory issues, as exemplified by the attempt to patent human genes ([15]). Although the U.S. Supreme Court has ruled against such patents, the patenting of gene-based risk-analysis methods remains viable at this time. The ultimate disposition of patent law and its implications for innovation and consumer welfare remain open questions. PrivacyPolicy makers, firms, and consumers have long wrestled with the problem of digital privacy. The problem has been amplified by the scope of ""Big Data"" and the ability of artificial intelligence to exploit it, with some fearing that autonomy itself is threatened by industry's resultant ability to characterize people's personalities and inclinations at such a granular level that behavior can be accurately predicted and surreptitiously manipulated ([35]). More recently, biometric techniques have provided marketers with additional opportunities to acquire and maintain customers ([22]). Although most forms of these biometric data (e.g., heart rate, eye movements) reside beyond our scope, genetic information resides squarely within it. Whereas some individuals may be quite willing to share their code ([47]), others may be less so. The public generally appears willing to share their genetic code for the purpose of exploring their ancestry but is appropriately concerned at the possibility that the same information could be used as a basis to deny employment or health insurance. Aversion to sharing one's genetic code may be especially high if the code is used to reveal dimensions of one's psychological self, as these dimensions speak to our very essence. Thus, the efforts made by marketers to solve the problem of digital privacy ([18]) should be applied to personal biology to prevent unauthorized collection, sharing, and use. Likewise, the intersection of privacy and autonomy needs to be examined from both a digital and biological perspective ([100]). FirmsBiology is poised to alter the landscape of a variety of businesses. The implications for managers can be distinguished in terms of who is potentially threatened by bioscience and who can leverage it. Threatened enterprisesIndustries that have been complicit in human misery are likely to be threatened by the public's increased understanding of bioscience. Consider again the case of hyperpalatable foods examined by [105]. The lay view of free will allows these firms to place responsibility for obesity and ill health at the feet of the consumer. An addiction model alters the playing field. Forward-looking firms should at least consider the possibility that lay beliefs regarding control and responsibility will shift with advances in biology, and firms should prepare for a more contentious relationship between policy and practice. Firms that market risky products to adolescents may be especially threatened by the public's understanding of bioscience. Analogous to how climate change influences organizational behavior, biology can also have sweeping influences on business practices. Thus, researchers who focus on managerial behavior can assess the extent to which firms are persuaded by the threats of biology and are inclined to adopt forward-looking policies.The wellness industry, too, faces threats from the public's understanding of bioscience. Industries that have positioned themselves as promoting well-being will need to confront the implications of bioscience for the believability of their claims. For example, the massive weight-loss industry can report little sustained success, as long-term weight reduction is rare ([25]).Health care more generally could face disruption. The U.S. health care model uses symptoms to diagnose a problem; the health care industry then profits from treatment. Genetics may change this traditional model because polygenic scores may help predict a problem and allow for a different type of intervention (e.g., prevention). Such a fundamental shift would alter not only the patient's clinical experience and health care professional's work experience but also the profit model of health care providers ([48]) and create downstream consequences for the insurance industry.Finally, the tumultuous education industry could face additional threats from biology. Educational institutions, especially those at the elite level, often tout the success of their students as a competitive differentiator. However, research shows that the superior outcomes of high-quality schools may be based on student selection. Specifically, [73] alleges that, after controlling for genetic effects, achievement is little affected by the quality of schools. Thus, if parents recognize that expensive schools add little value, private institutions, beginning with preschool, could be at risk, as could ancillary businesses that promise to increase the competitiveness of applicants to these institutions.Future research could examine whether consumer preferences and choices are altered by an increased understanding of biology. For example, in the case of health care, it remains to be seen whether consumers prefer to undertake actions to preempt the occurrence of ailments or to treat ailments as they arise. Moreover, in the case of education, a natural question is whether and how consumers are willing to trade off prestige against actual educational value provided to students. Potential beneficiariesIn terms of leveraging bioscience, much depends on how much consumers are willing to reveal. As we have noted, demands for confidentiality are likely to be especially high for biological characteristics that speak to consumers' essence or reveal their vulnerabilities. Insofar as consumers are willing to reveal polygenic indicators of psychological traits, for example, opportunities emerge for firms that provide a ""matching"" benefit (e.g., job placement, personal relationships). However, to be a viable contender in the matchmaking business, the genetic indicators need to improve to the point that they outperform traditional profiling methods.If widespread public appreciation of biology helps strengthen the hand of policy makers, increased funding would benefit providers of child-welfare services in both the public and private sectors. These providers include educational, counseling, and family-support services.The pharmaceutical industry may be able to address the underlying causality of ""psychological"" shortcomings by developing and marketing nootropic drugs. Such drugs are now widely available for problems involving anxiety, hyperactivity, and depression. In these cases, the lay public is receptive to biological causation, and the objective of the drug is to move the individual toward normal functioning. For many traits, however, the concern is that the pharmaceutical industry could market nootropic drugs not to enable normal functioning but to enhance the trait above its natural level. [77] found that people are reluctant to improve traits that are deemed essential, particularly when the alteration enhances the individual's natural ability. A question for future research is whether biological causation influences consumers' willingness to alter a trait. We took a tentative step toward addressing this question in the context of self-control (available from the authors). We found that biological causation of self-control increased participants' willingness to use a pharmaceutical to enhance their self-control. Future research needs to examine traits that are even more essential to a person's identity, such as integrity and generosity. ConsumersMany of the implications we have described converge on the consumer, but we note several others. First, knowledge of one's own biological constitution should have large effects on one's own behavior. Biology is not destiny, but swimming against the genetic tide is no simple matter. A polygenic score for BMI provides a salient example. Consumers who are not predisposed to a high BMI should feel encouraged to lower their weight if it becomes problematic; however, consumers who are predisposed to a high BMI should understand the nature of their battle. In terms of their time and monetary budgets, such information could guide consumers toward alternative routes to good health ([58]). At a minimum, such self-knowledge should challenge pervasive lay theories about the roles of diet and exercise ([56]).Second, appreciation of biological causation should influence attempts to alter the behavior of other consumers. Lay theories of self-control have previously been invoked to explain the likelihood that parents will engage in ""character-building"" policies ([64]), but these lay theories revolve around a belief in the inherent malleability of human self-control. Research in genetics addresses even more fundamental lay beliefs regarding the relative influences of nature and nurture on child development. [73] refers to ""the nature of nurture"" when describing the potential for misattribution. Consider reading behavior. Parents who read to their children may produce adults who like to read and are good readers, which may be taken as a causal effect of nurture. However, the heritability of traits bolsters the less intuitive role of nature in that parents and children may share a biological affinity for reading. The implication for consumer well-being is that happiness ensues when a parent's good intentions align with a child's dispositions; frustration and conflict may ensue otherwise.Taken together, a genetic perspective on biology can be beneficial to consumers by guiding them away from instrumental goals that are at odds with their biological predispositions, the result of which should be improved levels of success and contentment and perhaps even a greater sense of authenticity. Further, a genetic perspective prompts less criticism of self and others because failure to achieve a goal can be more appropriately attributed to a biological predisposition (rather than a lack of character); on the flip side, a genetic perspective can also instill a greater sense of accomplishment when success is achieved despite an opposing biological predisposition.Unique consumer implications of biology can also be derived from neuroscience. An exciting development is the potential contribution that mindfulness can make to consumer and societal well-being ([ 2]). For example, mindfulness meditation has been examined as a weight-loss treatment ([11]; [68]). It would be premature to make definitive statements about the efficacy of mindfulness training as an intervention, but we highlight several implications as they pertain to the present discussion. First, although the training itself can be characterized as a behavioral intervention, biological research has implicated a wide variety of potential neurological mediators ([38]; [89])—again illustrating the potential for biology and social science to inform and enrich each other. Second, regarding our discussion of impulsivity, recent research suggests that mindfulness training may temper emotional reactivity directly by dampening amygdala reactivity or indirectly through its effect on PFC–amygdala connectivity ([46]). Third, dualism appears impervious to this biological evidence for some devotees of meditation, as they are inclined to interpret the evidence in terms of the mind's influence on the physical brain ([39]).Ample research opportunities arise from the implications of biology on consumer well-being. For example, we find that scientific biological knowledge of self-control can shift lay beliefs of self-control, but its effect is behavior- and segment-dependent. Future research can examine whether the effect of scientific biological knowledge would influence actual self-control behavior and whether that effect would be behavior- or segment-dependent.In addition, the extent to which knowledgeable parents persist in parenting practices that go against their child's biological dispositions—and how to convince them to reset their expectations—are research questions of consequence. Similarly, it would be worthwhile for future research to investigate the extent to which appreciation of the biological basis of psychological interventions, such as mindfulness training, influences the real and perceived efficacy of the practice, particularly among those who do not hold strong dualistic beliefs and those who may be initially disinclined to adopt the practice. Marketing EducatorsMarketing education naturally reflects academic marketing research; consequently, the textbook approach to consumer behavior is dominated by the social sciences, particularly psychology. However, just as psychology itself is gravitating toward biology, we argue that marketing education should broaden ""consumer theory"" to include a biological perspective.We further maintain that a step in the direction of biology has benefits beyond a more complete understanding of consumers. We increasingly expect marketing students to emerge from their studies with a set of analytic skills. A comprehensive education would also leave students with a refined set of values. Marketing educators often address values under the heading of ethics, but we argue that students should be invited to consider biology and its implications for autonomy. Biology shifts the focus from the extent to which marketing diminishes consumers' autonomy (see the preceding discussion) to the amount of autonomy consumers had in the first place. As such, it goes well beyond the specific domain of ethics. The consumer and social welfare implications could be raised throughout the marketing education curriculum. To incorporate biology successfully into marketing education, marketing educators need not be biologists. The mechanistic details are far less important than the larger perspective of biological causation and its implications.Finally, marketing educators produce private-sector employees. Students who seek employment in the tobacco industry are aware of the physically addictive nature of nicotine. Students who wish to become brand managers at food companies may be less cognizant of the neurological effects of hyperpalatable foods. Informed students can plan their careers accordingly. ScholarsSelf-control has been a central concern to scholars of marketing (e.g., [ 1]; [23]; [94]) and associated disciplines (for a motivational view, see [ 3]]; for a cognitive view, see [13]]; for an economic view, see [90]]). Likewise, policy-oriented consumer research has long examined and advocated for messaging strategies that reduce self-defeating consumption. Such research is typically targeted at a specific behavior, such as tobacco, alcohol, or unhealthy food consumption ([69]). Some researchers have taken a broader approach by examining a class of behaviors (e.g., addiction; [49]) or a segment of consumers (e.g., adolescents; [71]). We endorse all these approaches but further argue that self-defeating behavior may have roots in neurological development, emerge across a spectrum of behaviors, and span the lifetime. As consumer researchers embrace the biological view, an important task is to educate both policy makers and the lay public about the biological antecedents of self-defeating behavior. Fortunately, this undertaking leverages marketing's communication and persuasion skills, which have proven effective at altering lay understanding of science ([104]). The endeavor also addresses the appeal for marketing to focus its efforts on matters of relevance and consequence ([61]).We also argue that this path is paved with opportunities for marketing scholarship. In the realm of communication and persuasion, our research reveals that biological causation runs counter to deep-seated views of self-control and raises very delicate questions about autonomy. How to convey the science and its human welfare and policy implications without prompting reactance is a multifaceted research question.At a more general level we concur that incorporating biology into marketing's conceptualization of self-control can create new opportunities for theory ([101]). The present research was inspired by the same question that made attribution theory a dominant force in social psychology: On what bases do people draw inferences about the cause of others' behavior? Most research in attribution theory draws a distinction between personal and situational causation, with a focus on the role of intentionality. However, this distinction is silent on the antecedent causes of intentionality. That is, even when a behavior is attributed to the actor, latter-day attribution models have been described as ""inert"" with respect to characterizing people's beliefs about the underlying reasons for the behavior ([55]).Revolutions in neuroscience and genetics promise to invigorate this question. Marketing scholars can play a central role not just as observers but as active participants. A first step is for marketing scholars to internalize the view ""that our minds are biologically based, rooted in banal physiological processes, and subject to the laws of nature"" ([39], p. 3). Once internalized, new meaning is given to the person–situation dichotomy that has dominated attribution theory for 70 years. A biological perspective fundamentally alters the concept of ""person"" by highlighting the individual's genetic blueprint and the neural processes and structures that drive behavior. A biological perspective fundamentally alters the concept of environment by closing the gap between the biological and psychological forces acting on the individual. Just as malnutrition alters brain development, so too does stress. A biological perspective greatly expands the notion of ""situation"" and its influences on behavior, including the impact of environment (e.g., adverse childhood experiences) on neural processes and structures, gene–environment correlations (i.e., self-selection of environment based on one's genetic constitution), and the many gene–environment interactions. ConclusionWe have argued that many of marketing's individual external stakeholders have much to gain from incorporating a biological perspective into their beliefs and practices. At a broader level, we are convinced that adoption of a biological perspective carries important implications for individual well-being, social equality, and national prosperity. Our empirical findings demonstrate, however, that biological causation is neither intuitive nor attractive to the lay mind. However, that gap between lay and scientific understanding is mutable, and we see marketing scholars as being a catalyst for change. We do not underestimate the communication and persuasion challenges ahead, but we also do not underestimate marketing's abilities. A first step is for marketing itself to become knowledgeable and comfortable with the role biology plays in self-control. By doing so, it can prepare itself for the process of change while adding explanatory depth to its long list of impressive behavioral findings.Beyond marketing's immediate stakeholders, we note the implications of biology for social conduct. A belief in a mechanistic world has been associated with reduced desire for retribution in the realm of justice ([84]). We contend that findings pertaining to justice can generalize to other contexts. A truer understanding of the biological underpinnings of behavior should reduce moral scolding and enhance empathy toward those who exhibit poor self-control and other ""failings""—including depression, irresoluteness, social awkwardness, infidelity, and even a lack of empathy—for which the biological and psychological causes are mistakenly dissociated. As the understanding of biological causation increases, so too should comity, mutual understanding, and societal well-being. " 13,Consumers and Artificial Intelligence: An Experiential Perspective," Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: ( 1) data capture, ( 2) classification, ( 3) delegation, and ( 4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations' investments into AI and to lay out an agenda for future research.","Not long ago, artificial intelligence (AI) was the stuff of science fiction. Now it is changing how consumers eat, sleep, work, play, and even date. Consider the diversity of interactions consumers might have with AI throughout the day, from Fitbit's fitness tracker and Alibaba's Tmall Genie smart speaker to Google Photo's editing suggestions and Spotify's music playlists. Given the growing ubiquity of AI in consumers' lives, marketers operate in organizations with a culture increasingly shaped by computer science. Software developers' objective of creating technical excellence, however, may not naturally align with marketers' objective of creating valued consumer experiences. For example, computer scientists often characterize algorithms as neutral tools evaluated on efficiency and accuracy ([62]), an approach that may overlook the social and individual complexities of the contexts in which AI is increasingly deployed. Thus, whereas AI can improve consumers' lives in very concrete and relevant ways, a failure to incorporate behavioral insight into technological developments may undermine consumers' experiences with AI.This article aims to bridge these two perspectives: on one side, we acknowledge the benefits that AI can provide to consumers. On the other side, we build on and integrate sociological and psychological scholarship to examine the costs consumers can experience in their interactions with AI. Exposing the tension between these benefits and costs, we offer recommendations to guide managers and scholars investigating these challenges. In so doing, we respond to the call from the Marketing Science Institute to examine ""the role of the human/tech interface in marketing strategy"" and to offer more scholarly attention to situations where ""customers face an array of new devices with which to interact with firms, fundamentally altering the purchase experience"" (Marketing Science [98]).We begin by offering a framework that conceptualizes AI as an ecosystem with four capabilities. We focus on the consumer experience of these capabilities, including the tensions felt. We then offer more insights into the experience of these tensions at a macro level by exposing relevant and often explosive narratives in the sociological context and at the micro level by illustrating them with real-life examples grounded in relevant psychological literature. Using these insights, we provide marketers with recommendations regarding how to learn about and manage the tensions. Paralleling the joint emphasis on social and individual responses, we make recommendations outlining both the organizational learning in which firms should engage to lead the deployment of consumer AI and the concrete steps they should take to design improved consumer AI experiences. We close with a research agenda that cuts across the four consumer experiences and suggests ideas for how researchers might contribute new knowledge on this important topic. Understanding the Consumer AI ExperienceWe conceptualize AI as an ecosystem comprising three fundamental elements—data collection and storage, statistical and computational techniques, and output systems—that enable products and services to perform tasks typically understood as requiring intelligence and autonomous decision making on behalf of humans ([ 3]). These elements are associated with capabilities (i.e., listening, predicting, producing, and communicating). Data collection devices listen in the broad sense of gathering information from different sources; for example, product sensors scan the environment, and wearable devices record physical activity. Algorithms leverage this information to predict; for example, Spotify serves music suggestions through personalized playlists. Finally, output systems produce a response or communicate with consumers, for example by directing a vehicle or responding through consumer interfaces like Baidu's Duer.To articulate a customer-centric view of AI, we move attention away from the technology toward how the AI capabilities are experienced by consumers. ""Consumer experience"" relates to the interactions between the consumer and the company during the customer journey and encompasses multiple dimensions: emotional, cognitive, behavioral, sensorial, and social ([19]; [92]). Our framework is built on four experiences that reflect how consumers interact with the four AI capabilities (Figure 1). This experiential perspective helps shed light on the affective and symbolic aspects of technology consumption in addition to the utilitarian and functional ones ([107]). ""Data capture"" is the experience of endowing individual data to AI, ""classification"" is the experience of receiving AI's personalized predictions, ""delegation"" is the experience of engaging in production processes where the AI performs some tasks on behalf of the consumer, and ""social"" is the experience of interactive communication with an AI partner.Graph: Figure 1. The consumer AI experience.For each experience, we identify benefits and costs from a consumer perspective and propose that managers qualify their focus on the former by paying attention to the latter: a data capture experience may serve or exploit consumers, a classification experience may understand or misunderstand them, a delegation experience may empower or replace consumers, and a social experience may connect or alienate them. We next examine each of these experiences, their social science connections, managerial implications, and future research directions. The AI Data Capture ExperienceThe listening capability enables AI systems to collect data about consumers and the environment in which they live. We conceptualize the resulting experience as ""data capture,"" which includes the different ways in which data are transferred to the AI. Data can be intentionally provided by consumers, albeit with different degrees of understanding of the process: consumers share data when there is little or no uncertainty about how the data will be used and by whom, or consumers surrender data when this uncertainty is high ([147]). Data can also be obtained by AI from the ""shadows"" consumers leave behind when they engage in daily activities, as in the case of a shopper perusing a store equipped with facial recognition technology or of an iRobot Roomba creating a map of a residential space ([89]).The data capture experience provides benefits to consumers because it can make them feel as if they are served by the AI: the provision of personal data allows consumers access to customized services, information, and entertainment, often for free. For example, consumers who install the Google Photos app let Google capture their memories but in return get an AI-powered assistant that suggests context-sensitive actions when viewing photos. Access to customized services also implies that consumers can enjoy the outcome of decisions made by digital assistants, which effectively match personal preferences with available options without having to endure the cognitive and affective fatigue that decision making can entail ([ 4]). Finally, access to customized services offers unprecedented opportunities for self-improvement. Consider one of the projects within Alphabet, in which data from smartphones, genomes, wearables, and ambient sensors are combined to drive personalized health care ([86]).Despite AI's ability to predict and satisfy preferences, consumers can feel exploited in data capture experiences, mainly because they do not understand AI's operating criteria. This can be attributed to several features of AI. First, the modalities of data acquisition are becoming increasingly intrusive and difficult to avoid. Second, even when consumers intentionally share information, they are not aware of how this information is aggregated over time and across contexts. Finally, data brokers are largely unregulated and often lack transparency and accountability ([59]). As a result, data capture experiences may threaten consumers' ownership of personal data and challenge personal control, that is, the feeling that events are determined by the self rather than by others or by external forces and can be stirred toward desired outcomes ([36]). We examine the consequences of this loss of control next from both a sociological and psychological perspective. Sociological Context: The Surveillance Society NarrativeIn popular culture, lack of ownership over personal data has been frequently associated with a loss of personal control stemming from technology's threatening potential to enable monitoring of human behavior. Stories such as George Orwell's 1984 or Philip K. Dick's Minority Report envision systems of oppression in which, due to lack of privacy and constant surveillance, people can no longer control their destiny. This dystopian imagination is echoed in sociological scholarship that associates data capture with the rise of a capitalist marketplace in which private information becomes the central form of capital ([157]).Such dystopian concerns strike a resonant chord when considering Google's move in the early 2000s to transform consumer data from a by-product into an economic asset that formed the basis of a new type of commerce driven by the ability to colonize the consumer's private experience. This commerce contributes to a surveillance marketplace, in which data surplus is ""fed into advanced manufacturing processes known as 'machine intelligence' and fabricated into prediction products that anticipate what you will do now, soon, and later"" ([157], p. 14, italics in the original). To illustrate the power of this commerce, targeted ads based on personality characteristics inferred from the analysis of Facebook likes in combination with online survey questions can increase conversion rates by about 50% ([102]). In 2018, Facebook's revenues from the sales of such tailored ads was close to $56 billion ([111]).From the perspective of this narrative, not only are technology companies continually required to find new ways to make monitoring and surveillance palatable to consumers by linking it to convenience, productivity, safety, or health and well-being ([10]), but they must also constantly push the boundaries of what private information consumers should share ([55]) through a complex landscape of notifications, reminders, and nudges intended to initiate behavioral change. Thus, as consumer behavior becomes increasingly retailored to the exigencies of behavioral futures, AI can transform consumers into subjects who are complicit in the commercial exploitation of their own private experience, thereby undermining personal control and promoting the concentration of knowledge and power in the hands of those who own their information. Psychological Perspective: The Exploited ConsumerData capture experiences are characterized by an underlying tension: consumers recognize that data capture allows AI to serve them through customization, but AI's inherent lack of transparency makes them feel exploited. These feelings of exploitation are fueled by actual and perceived loss of personal control, with important psychological consequences ([17]). The first of such consequences is negative affect, which can turn into demotivation and helplessness. Consider the case of Leila, a sex worker who shielded her identity on her Facebook account and reported being shocked to see some of her regular clients recommended by the ""People You May Know"" function. According to Leila, ""the worst nightmare of sex workers is to have your real name out there, and Facebook connecting people like this is the harbinger of that nightmare."" For Leila, like for domestic violence victims or political activists, privacy invasion is not only frightening, it may become a matter of life, death, or time in jail ([73]).As being in control is a basic need and a precondition of psychological welfare ([93]), the second consequence of loss of personal control may be moral outrage. Consider the case of a German consumer who requested his own data from Amazon and received transcripts of Alexa's interpretations of voice commands, even though he did not own any Alexa devices. The consumer relayed his story to a local magazine, which attempted to identify the consumer whose privacy had been compromised. The magazine staff involved in this experience described it as follows: ""[we were able to] navigate around a complete stranger's private life without his knowledge, and the immoral, almost voyeuristic nature of what we were doing got our hair standing on end"" ([24]).The third consequence of loss of personal control relevant to data capture experiences is psychological reactance, a state in which a person is motivated to restore control after a restriction ([21]), which causes more negative evaluations of and hostile behaviors toward the source of the restriction. In marketing, reactance can decrease the likelihood to repurchase and follow recommendations ([48]). Illustrating reactance in AI data capture experience is Danielle, a U.S. consumer who installed Echo devices throughout her home, believing Amazon's claims that they would not invade her privacy. When one of her Alexas recorded a private conversation and sent it to a random number in her address book, Danielle said ""I felt invaded"" and concluded, ""I'm never plugging that device in again, because I can't trust it"" ([76]).In summary, consumers may experience data capture as a form of exploitation: whereas technology companies, firms, and governmental agencies gain financial and political power, consumers lose ownership of their data and feel a loss of control over their lives. As we discuss next, managers should gain a better understanding of feelings of exploitation, as they prevent consumers from seeing the value firms can provide through data capture. This understanding starts at the organizational level and is then translated into decisions about experience design. Managerial Recommendations: Understanding the Exploited Consumer Organizational learningA central programmatic task in addressing the issue of consumer exploitation in AI data capture experiences involves determining and enhancing the organization's level of awareness regarding the sociological and psychological costs raised in the previous sections. Companies should strive toward greater organizational sensitivity around consumer privacy and the current asymmetry in the level of control over personal data. For instance, they should use netnographic observation or sentiment analysis to listen empathetically and at scale to consumers who have experienced exploitation in AI data capture experiences. Furthermore, rather than accepting the surveillance society narrative at face value, firms can use these tools to understand when, how, and whether their own data capture experiences play into versus subvert this narrative. Likewise, companies should draw on insights by privacy scholars and activist movements to question their taken-for-granted beliefs. In doing so, for instance, companies could realize that their own view on privacy default settings might differ markedly from that of a vulnerable consumer group and adjust their processes accordingly ([101]).Organizational learning can also extend beyond the boundaries of the individual firm to encompass other institutions. First, companies could sponsor research aimed at understanding the influence of surveillance society–style thinking on their culture and practice, as well as its negative impact on marketing activities and consumers. Second, companies could adopt a more communal approach to sharing individual organizational learning with other firms, industry associations, educators, and the media. Third, industry groups could collaborate with scholars to create and adopt an algorithm bill of rights for individuals ([77]), which some AI experts have proposed should include a right to transparency, for example, ""the right to know when an algorithm is making a decision about us, which factors are being considered by the algorithm, and how those factors are being weighted"" ([127]). Experience designUsing this organizational learning, organizations should design improved AI data capture experiences. Recent regulations, such as the European Union's General Data Protection Regulation, aim to limit exploitation by making organizations responsible for giving consumers the possibility to opt into specific data collection processes (e.g., cookies) and to ask for greater clarity on how these data are used.However, as AI becomes more pervasive and ubiquitous, ensuring consumer consent at all steps of the customer journey may result in an overload of choice and information that decreases instead of increases personal control ([81]) and exacerbates the negative affective and behavioral reactions illustrated previously. Interventions related to the way in which options are presented—the choice architecture—can reduce the cognitive and affective costs associated with excessive information and choice ([30]) and thereby give consumers greater control over their data without overloading them.Among such interventions, including default options has proven especially effective in facilitating decision making as well as influencing specific behaviors ([140]). Because individuals tend to passively accept defaults instead of exercising their right to opt out, the selection of defaults by choice architects may lead to suboptimal outcomes when it does not properly consider preference heterogeneity. The personalization of defaults could mitigate this issue ([137]), and AI itself could assist consumers in the automatic implementation of preferences about how their data are captured and analyzed.More broadly, organizations can limit consumer exploitation by playing an active role in educating consumers about the costs and benefits entailed in AI data capture experiences. For example, the recently overhauled Google Home app clearly communicates what user data have been stored and why. Understanding the potential for exploitation in data capture experiences is useful not only for managers interested in maximizing the value provided to consumers served by the AI but also for researchers interested in uncovering the sociological and psychological underpinnings of the tension that accompanies this experience. Future Research on the AI Data Capture Experience Sociological research questionsFuture research should investigate how sociocultural forces affect feelings of exploitation in data capture experiences. People from poorer childhood backgrounds have a lower sense of control than those from wealthier ones ([109]), and collective self-construal is associated with a lower desire for choice freedom and control ([ 9]; [100]). Thus, both consumers' socioeconomic status (Research Question 1A, or RQA1; see Table 1) and prevailing cultural norms (RQA2) could influence consumers' propensity to feel and be exploited by AI. Other factors, such as education, political orientation, gender, and race (RQA3) could be examined using an intersectionality lens ([32]).GraphTable 1. Consumers and AI Experience: Emerging Research Questions (RQs). Future research should also explore how the cultural cognitive, normative, or regulatory legitimacy of AI changes over time to influence consumer reactions to data capture ([ 1]; [79]), particularly in light of AI's rapid diffusion in the marketplace. For example, researchers could study how and when increasing levels of familiarity with AI may reduce consumer sensitivity toward exploitation (RQA4). Psychological research questionsAn interesting avenue for future research consists of exploring the role that psychological processes play in interpreting AI data capture experiences as exploitative. For example, researchers could study the role of motivated reasoning ([88]) in shaping consumer affective reactions to data capture experiences (RQA5): strongly held goals may motivate consumers to accept greater risk of exploitation when the AI is seen as a conduit to goal completion, mitigating negative emotional responses.Other important open questions concern how the source and type of data used by the AI affect its potential to exploit. For example, an AI-enabled device that is constantly listening to biometric data could, over time, become paradoxically less invasive than one that listens only when activated ([142]). Complementing recent scholarship on the consequences of personal quantification ([42]), future research should address how the frequency of data capture (e.g., intermittent vs. continuous) affects perceived exploitation (RQA6). As another example, information collected about the physical environment, such as that acquired by a smart refrigerator, may be less likely to generate feelings of exploitation than information collected about the self, such as that acquired by a fitness tracker (RQA7).Feelings of exploitation may also differ on the basis of the physical context of consumption (RQA8). Current attempts by companies like Amazon or Google to redefine the family home as a space accessible to corporations rather than a private space may attenuate or exacerbate these feelings. Similarly, physical features of the environment where data collection takes place may differently trigger concerns about exploitation. For example, crowded environments lead to a loss of perceived control, which could decrease willingness to provide data. Concerns about exploitation may also differ on the basis of the device used to interact with AI (RQA9), as research has shown that consumers are more likely to self-disclose when using smartphones versus PCs ([105]).Finally, when consumers cannot or do not want to take advantage of the benefits of data capture, psychological reactance toward AI may manifest in adversarial user behaviors, as suggested by the experience of Danielle. Future research can explore the factors that lead consumers to respond to feelings of exploitation with behaviors like sabotaging AI by disabling sensors' inputs, intentionally providing false data by creating fake user profiles, or adopting antisurveillance outerwear to confuse the algorithms controlling facial recognition systems (RQA9). The AI Classification ExperienceFirms leverage the predicting capability of AI to create ultra-customized offerings and maximize engagement, relevance, and satisfaction ([87]). Sophisticated algorithms consider a wide variety of information, including the characteristics of both current and past consumers. For example, Netflix uses AI to offer personalized movie recommendations based on not only individuals' past viewing history and that of other viewers but also contextual information such as day of the week, time of day, device, and location ([83]). Netflix even uses AI to select videoframe thumbnails that can increase subscribers' likelihood to click on a specific show ([154]). Even though prediction interfaces use individual and contextual information, they often refer to information related to other users either explicitly by mentioning others when framing recommendations (e.g., Amazon noting ""customers who bought this also bought"") or implicitly by organizing recommendations in terms of communities of users or taste niches (e.g., Amazon Prime drawing attention to movies for ""period drama fans""). As consumers are often unaware of the workings of algorithms, they may infer that these recommendations are based on being classified as a certain type of person. Such inferences are amplified by the human tendency for categorical thinking in person- and self-perception ([143]). For example, consumers engage in categorical inference making when they are served behaviorally targeted ads: they attribute the ads they receive to the advertiser labeling them as a person with specific tastes ([136]). We conceptualize the ""classification experience"" as one in which consumers perceive AI-enabled personalized predictions to be the result of being classified as a certain consumer type.Classification experiences can be positive because they lead consumers to feel deeply understood either objectively or subjectively. For example, consumer categorizations can be valuable to affirm the self: personalized offers that indicate membership in an aspirational group may help consumers satisfy identity motives when they are perceived as social labels ([136]). Framings based on other users, such as ""people who like this also like,"" make recommendations more persuasive than those based on the product, such as ""similar to this item"" ([54]), further suggesting that the experience of feeling classified by AI as a certain type of person is often positive. These findings resonate with research demonstrating the psychological benefits of group membership ([124]; [143]). However, classification experiences may also lead consumers to feel misunderstood when they perceive AI as having inaccurately assigned them to a group or as having made biased predictions on the basis of group assignment. At the societal level, classification by AI is linked to a dystopic narrative in which access to resources and freedom is restricted for some groups. Sociological Context: The Unequal Worlds NarrativeClassification experiences do not exist in a sociological vacuum but are shaped by popular myths. Science fiction stories such as Neill Blomkamp's Elysium have routinely imagined deeply divided police states in which the ruling class draws on algorithms to sustain a regime of inequality and fear. Sociological scholarship on the politics of algorithms ([133]) has also drawn on this popular imagination to theorize AI in the context of rationalization and quantification ([122]), automated inequality ([38]), uneven information landscapes ([43]), and the historical rise of ""algorithms of oppression"" ([115]) or ""weapons of math destruction"" ([117]). Emphasizing the intersectionality of race and gender with antisemitism, poverty, unemployment, and social class ([32]), these investigations of AI's potential for social classification are particularly insightful. AI is feared to privilege whiteness and undermine the identity projects of minorities ([39]). This contention is consistent with research on the market (bio)politics of race, which has consistently shown the inherently discriminatory potential of marketized representations of culture and ethnicity, and it is also supported by economic critiques that warn against the monopolization of information by a centralized system ([70]; [121]).Consider Google's corporate mission to ""organize the world's information."" From an unequal worlds perspective, such a statement is far from politically neutral; rather, it exemplifies the operation of seemingly benign appeals to data automation and quantification in a market that sanctions the production of biased information. In such an ideological system, the designers of an AI-enabled college admissions software, for instance, may be convinced that AI can help combat human selection bias. However, because ""algorithms that rank and prioritize for profits compromise our ability to engage with complicated ideas"" ([115], p. 118), the resulting AI experience may not only reduce the complex experiences of targeted marginalized populations to a set of more simplified sociodemographic attributes or stereotypes but it may also unintentionally expose marginalized applicants to racial profiling, misrepresentation, and economic redlining when used by admissions officers. Likewise, problems can arise when banks use AI to decide whether a consumer is worthy of borrowing money. Although algorithms may make the selection process more efficient, they can also systematically exclude consumers who live in a neighborhood with higher credit defaults ([23]). The realization that AI can result in racial and social groups experiencing discrimination is an important backdrop for a psychological analysis of consumers' feelings of being misunderstood. Psychological Perspective: The Misunderstood ConsumerClassification experiences are characterized by an underlying tension between feeling understood and misunderstood. Consumers can feel misunderstood because of perceived incorrect classification, discriminatory use of classification, or a combination of the two. First, consumers are likely to feel misunderstood when they perceive the identity implied by the AI's output as incorrect, either because it is factually inaccurate or because it is based only on one identity, whereas most individuals identify with a host of personal and social selves ([120]). Identity-based consumer behavior is often the result of a negotiation between belonging and uniqueness motives playing out across this constellation of identities ([29]). In situations where consumers perceive AI predictions to be driven by their membership in a group, uniqueness motives may become relatively more salient. When this happens, group identity appeals may backfire if they are believed to threaten individual agency ([12]). This negative response is especially likely when the consumer perceives the identity assigned to them by the AI as noncentral or dated, as in this excerpt from a Spotify Community post ([60]):""The recommendations s*ck:- Listened to a few anime covers, now all my ""Discover Weekly"" is filled with disgusting covers. I'm trying to ""not like"" all of them, but it doesn't work .... I've stopped listening to rock years ago and still get rock recommendations.""From this consumer's perspective, the AI used by Spotify seems to have decided that they like anime covers and rock, putting them in a category that they reject or do not see as capturing their multifaceted and evolving self. The consumer is frustrated not only with being misunderstood by the AI, but also with their perceived inability to alter such misunderstanding.Second, consumers may also feel misunderstood when they fear AI is using a social category in a discriminatory way to make biased predictions about them. This is particularly problematic in contexts where these predictions may enhance consumers' vulnerability because they restrict access to marketplace resources ([74]). For example, fintech companies increasingly use easily accessible digital information such as individuals registering on a webpage to predict their payment behavior and defaults and therefore judge their creditworthiness ([ 8]). Consider this tweet by a software developer, David Heinemeier Hansson (@dhh, November 7, 2019, https://twitter.com/dhh/status/1192540900393705474):""The @AppleCard is such a f*ing sexist program. My wife and I filed joint tax returns, live in a community-property state, and have been married for a long time. Yet Apple's black box algorithm thinks I deserve 20x the credit limit she does...""This consumer is frustrated because of the AI's inability to understand the reality of his household's finances, but he is also morally outraged because he thinks that his wife's denial of credit was based on her gender. Perception of vulnerability such as this can have negative effects on the self-concept. This can occur, for example, when minorities whose financial choices are systemically restricted then frame the self as ""fettered, alone, discriminated, and subservient"" and experience reductions in self-esteem and self-efficacy ([13]).Consumers can also experience a combination of the two ways of feeling misunderstood mentioned previously: they can be incorrectly assigned to a category and this incorrect assignment can exacerbate existing limitations on choice and freedom for vulnerable consumers. Facial recognition software, for instance, uses AI to identify a person by comparing a target facial signature to databases of known images. The range of applications of such software includes mobile devices (e.g., Apple's Face ID), social media (e.g., Facebook's tagging feature), and physical spaces (e.g., airport customs officials). Whereas a failure of Apple's Face ID to start one's own device may result in frustration, incorrect identification in other applications may result in ethical violations. Consider the open letter to Amazon CEO Jeff Bezos written by the Congressional Black Caucus on the potential danger caused by Amazon's facial recognition tool, Rekognition:Communities of color are more heavily and aggressively policed than white communities....We are seriously concerned that wrong decisions will be made due to the skewed data set produced by what we view as unfair and, at times, unconstitutional policing practices. ([125])In a subsequent test, Rekognition indeed incorrectly matched 28 current members of the U.S. Congress with people who had committed a crime, and the false matches were disproportionately for people of color ([134]). In June 2020, Amazon suspended police use of this technology ([47]). We next examine how managers can understand and address the risk of consumers feeling misunderstood. Managerial Recommendations: Understanding the Misunderstood Consumer Organizational learningHow does an organization best surface and address accounts of biased treatment? Unlike data capture errors, which may be lagged and hard to correct in real-time, classification errors produce signals soon after they occur. They also happen in very different parts of an organization. For instance, if an AI system has rejected a college applicant due to a biased algorithm, it is likely to assume that such a classification error will almost immediately surface in the college's admissions department and data—data that in turn might be used to structure the next round of applications.Owing to this data dependency, organizations may not even be aware that a given distribution or algorithm is the result of a classification error. In the case of a college, for instance, classification might be regarded as a natural outcome of the competitive process by those in charge of managing the admissions process. Thus, unlike data capture failings that require the specific attention of software programmers and data scientists, addressing classification errors requires organizations to focus on marketing and consumer-facing departments and to examine whether these departments' databases or, more abstractly, the organizations' taken-for-granted understanding about whom they have served and should serve and why, carry entrenched social and racial biases.Organizations must thus focus on learning about the specific biases that might be present in their own algorithms and processes to root them out. In the United States, the Algorithmic Accountability Act of 2019 would require companies to assess their AI systems for ""risks of 'inaccurate, unfair, biased, or discriminatory decisions' and to 'reasonably address' the results of their assessments"" ([97], p. 1). Rather than reacting to a changing regulatory landscape, firms should proactively collaborate with technology experts and thought leaders in computer science, sociology, and psychology to develop and conduct such audits. Firms can then share both their audit processes and outcomes, for example by engaging in lobbying efforts to ensure that regulations passed in the name of consumer welfare include meaningful and technologically appropriate provisions to protect consumers from discrimination. Experience designOrganizational learning should be leveraged in the design phase to develop AI classification experiences that minimize consumers' likelihood of feeling misunderstood. Managers could build on the insights gained from listening to consumers who felt they were classified on the basis of narrowly defined identities to experiment with diversifying and broadening the content they provide and to propose products that are dissimilar from the user's preference profile. Indeed, Spotify has launched Taste Breakers, a function that introduces customers to music to which they normally do not listen. Similar attempts at ""bursting the bubble"" are especially important in light of the possibility that, by optimizing information provision on the basis of past choices, AI both ignores long-term goals that do not reflect short-term behaviors ([ 4]) and increases attitude extremity and polarization ([49]). Firms could also address feelings of being misunderstood by asking consumers to validate AI-based inferences. As greater user participation in the implementation of algorithms increases satisfaction in decision support systems ([151]), periodically offering consumers the opportunity to update the AI's view of the self could similarly reduce potential frustration.Managers can build on the insights gained from listening to discriminated consumers to design both debiased and antibias AI experiences that foster an inclusive society rather than perpetuate inequality ([62]). To do so, managers should institute protocols that swiftly react to any bias uncovered in regular audits of the AI systems for the presence of discrimination ([156]). Organizations should also diversify their hiring to include more members of social minority groups and ensure that their culture and processes represent diverse viewpoints at all stages of the design of AI classification experiences. For example, advocates for reducing bias in AI have suggested that technology companies must employ more individuals with disabilities to learn how to eliminate disability bias from AI ([31]). The tension between feeling understood and misunderstood in classification experiences represents a learning opportunity not only for managers but also for researchers. Future Research on the AI Classification Experience Sociological research questionsResearchers can unpack the influence of sociocultural factors on classification experiences. Values and ideology may change consumers' interpretation of personalized predictions, as those who are more aware of the sociohistorical context of discrimination by algorithm ([115]) and belong to marginalized groups should also feel more vulnerable to AI's potential to restrict access to resources and freedom (RQB1).Drawing on research that examines the ways in which powerful institutions define the consumer ([16]), future work should also explore the social classifications that firms routinely inscribe into their AI solutions, such as certain consumers' habits, norms, and preferences. This lens can usefully unearth the existence of ideological blind spots in the models employed by firms and examine the uneven landscapes of experiences and choices that these models produce when consumers are subjected to them (RQB2). Psychological research questionsFuture research should explore how psychological processes affect the extent to which consumers feel misunderstood in classification experiences. Open questions concern lay beliefs about how organizations create AI classifications (RQB3) and whether certain inferred categorizations are especially likely to induce feelings of being misunderstood (RQB4). For example, research on attributional ambiguity suggests that stigmatized consumers may attribute AI classifications to bias toward their group identity on the part of the algorithm rather than to other causes ([33]).More generally, feeling misunderstood may be more likely in contexts where consumers value uniqueness over belongingness (RQB5). For example, patients are reluctant to use medical AI due to a sense that it cannot account for their unique characteristics and circumstances as well as human doctors can ([95]). The nature of a task may also have an influence (RQB6): Consumers tend to exhibit greater aversion toward algorithms for subjective tasks, which are based on personal opinions or intuitions, than for objective ones, which are based on quantifiable and measurable facts ([28]). Given that many AI systems learn and predict subjective taste, negative reactions to inferred classification might be especially common. The AI Delegation ExperienceA ""delegation experience"" is one in which consumers involve an AI solution in a production process to perform tasks they would have otherwise performed themselves. These tasks can be decisions, such as when Google Assistant, at the consumer's request, calls a hairdresser, matches the consumer and the hairdresser's calendars, and uses a human-like voice to book an appointment. They can also be actions in the digital world, like those performed by Smart Compose, a writing tool that uses AI to help consumers write emails. Finally, they can be actions in the physical world, such as when the Nest Thermostat learns the consumer's temperature preferences and programs itself to fit them.By not having to engage in the tasks the AI performs on their behalf, consumers in delegation experiences can feel empowered in two distinct ways. First, consumers can spend their time and effort on activities they find more satisfactory and meaningful: they can work less and enjoy the positive effects of leisure ([46]), or they can work better and enjoy greater happiness by delegating extrinsically motivated tasks to AI and keeping intrinsically motivated tasks for themselves ([18]). Second, consumers can focus on activities that are more suitable to their skills and leave to AI those on which they underperform. This way, they can enhance self-efficacy, or the perceived ability to master the environment to produce a desired outcome ([ 5]).Given the empowering benefits of delegation experiences, managers may be tempted to offer consumers increasingly more opportunities to delegate tasks to AI. However, like the case in which the mere presence of too many choice options can reduce consumers' satisfaction ([81]), the mere presence of too many delegation opportunities may lead to aversive consequences. We next examine this tension between the possibility of AI to both empower and replace consumers both at the societal and individual level. Sociological Context: The Transhumanist NarrativeTo analyze the negative aspects of delegation brought about by the possibility of being replaced from a sociological perspective, it is helpful to examine how the heuristics that have guided consumers' interactions with AI tools have been historically understood in popular culture. We draw on widespread science fiction and social science literature that falls into the so-called ""transhumanist"" genre. From Fritz Lang's Metropolis to Isaac Asimov's I, Robot, and from Mary Shelley's Gothic Frankenstein to James Cameron's Terminator, countless cautionary tales have profiled the dangers of reimagining human capabilities and characteristics through a technological mirror. Specifically, these stories fuel the view that, by transcending human limitations, technology eventually molds into an omnipotent superhuman and subsequently constitutes the ideal of technological perfection—implying new standards.Critics of this transhumanist perspective ([129], p. 23) have linked AI to ""new logics of expulsion"" and economic redundancy that arise as AI approaches aging, health, productivity, and other domains through the transhumanist lens of limitless performance rather than standard levels of well-being or productivity. These observers fear that AI solutions will result in significant unemployment, leading to a rapid increase in surplus populations whose AI experience will be their de facto removal from the productive aspects of the social world.In the social science literature, this superhuman narrative is paralleled in the Computers Are Social Actors and Human Computer Interactions paradigms, according to which the same heuristics used for human interactions are mindlessly applied to computers ([63]; [114]). Since the 1960s, technology companies have periodically imbued the productive aspects of AI technology and machine prototypes with mythic narratives emphasizing that science and technology will eventually accomplish human immortality.These transhumanist ideas, which emphasize technological progress as an unstoppable force that alters human experience ([71]), have been deeply inscribed in contemporary AI experiences, from the promise that the Roomba vacuum cleaner could perform tasks more effectively than humans to the promise that 23andMe could help in the creation of genetically optimized offspring. However, the transhumanist preoccupation with Promethean aims underlying many contemporary AI experiences also leads to systemic dehumanization ([53]; [64]). For instance, human perception of mastery over the environment depends on not being subject to unilaterally imposed specifications. A world in which our interactions with machines are fueled by transhumanist ideals will endorse a glorification of capitalism's endless creativity while treating destructiveness and human replacement as normal costs of doing business ([131]). Furthermore, an economic obsession with ""perfection,"" ""progress,"" and ""efficiency"" will promote the rise of the ""useless class"" ([66]), individuals whose skills are no longer developed or demanded, thus fundamentally eroding democracy and social justice. Psychological Perspective: The Replaced ConsumerDelegation experiences can help consumers feel empowered but can also raise concerns about being replaced. The mere recognition of AI's capability to act as a substitute for human labor can be psychologically threatening for three main reasons. First, people have a strong desire to attribute consumption outcomes to one's own skills and effort ([ 5]; [94]). Research on human–computer interaction has shown that humans often see computers as disempowering because they deprive humans of the sense of accomplishment related to an activity, so much so that humans tend to credit themselves for positive outcomes and blame computers for negative ones ([110]). In contexts where products are crucial to the experience of having an identity as a certain type of person ([124]), delegation experiences may feel tantamount to cheating. In the fishing industry, for example, AI can help anglers be more effective in location and bait decisions. However, in the words of biologist Culum Brown:It is really getting kind of unfair. If you are going to use GPS to take you to a location, sonar to identify the fish and a lure which reflects light that humans can't even see, you may as well just go to McDonald's and order a fish sandwich. ([40])Second, outsourcing labor to machines prevents consumers from practicing and improving their skills, which can negatively influence self-worth and contribute to a satisficing tendency by which individuals settle for a level of engagement that is just good enough. Consider the experience of journalist John Seabrook. While composing an email to his son, Seabrook started the sentence ""I am p...,"" intending to write ""I am pleased,"" but resolved to instead accept the suggestion of Google's Smart Compose ""I am proud of you."" After hitting Tab to accept the suggestion, [132] muses:What have I done? Had my computer become my co-writer? That's one small step forward for artificial intelligence, but was it also one step backward for my own?...I'd always finished my thought by typing the sentence to a full stop, as though I were defending humanity's exclusive right to writing, an ability unique to our species. I will gladly let Google predict the fastest route from Brooklyn to Boston, but if I allowed its algorithms to navigate to the end of my sentences how long would it be before the machine started thinking for me?Finally, outsourcing tasks to AI can lead consumers to experience a loss of self-efficacy. Self-efficacy is an antecedent of personal control ([ 5]), and it is heightened when individuals are actively engaged in creative tasks ([35]; [116]). The notion that being productive is a way to feel in control is consistent with findings showing that consumers who experience low control attempt to reestablish it by choosing products that require higher, versus lower, effort to achieve a desired outcome ([34]). In line with this view that delegation can lead to loss of control, drivers involved in GPS-related accidents tend to describe their experience in terms of surrendering control to the machine. Take for instance the tourists who drove their car into the ocean trying to reach an Australian island and recounted that the GPS ""told us we could drive down there...It kept saying it would navigate us to a road"" ([108]).The tension between being empowered and replaced is relevant from a managerial perspective because AI designers need to decide how delegation experiences should be designed to protect self-efficacy and self-identity. We next discuss potential recommendations emerging from the sociological and psychological analysis of this tension. Managerial Recommendations: Understanding the Replaced Consumer Organizational learningCompanies can start by learning how to integrate the human desire for self-efficacy into corporate discourse in two main ways. First, they can collaborate with family scholars, workplace psychologists, and health sociologists to understand the consequences of human replacement by AI. Second, they can engage in conversations with consumers to gain greater insight into which activities they prefer to reserve for themselves versus delegate to AI, and how these preferences shift across consumer, identity, and task. Organizational design and personnel policies can facilitate this learning by ensuring that the insights gained through external collaborations and consumer listening permeate the firm's culture, especially in the more technical functions. For instance, technology firms could hire experts in creativity such as artists, artisans, or chefs into AI-focused experience design roles.Firms could also learn from organizations that protect, support, and enhance abilities that are conceived as intrinsically ""human"" and on which individuals remain superior to machines, such as performing complex tasks, adapting to changes, using emotional intelligence, and offering nuanced judgments in unstructured environments ([78]). Thus, collaborations with museums, theaters, and universities' humanities departments can inspire managers to understand how AI can preserve, rather than subvert, traditional human values such as creativity, collaboration, and community ([25]). Experience designThe learning achieved in the previous phase should serve as the bedrock on which AI designers decide how to model delegation experiences to protect self-efficacy and self-identity ([94]). Division of labor in production processes can have positive effects on demand if consumers feel they have the competence to make sound decisions about the tasks in which they decide to engage ([52]). Thus, AI can be conceived as a platform to enhance intrinsically human skills and values. In the medical domain, for example, the benefits of AI-powered surgical robots for consumers depend on the way in which the surgeon's input and supervision is designed. Surgical robots are more precise than humans, can make quicker and more reliable diagnoses, and are more democratic and cost-efficient than current systems because they can intervene outside of hospitals. Still, the structure of surgeons' supervision of the robots is central to the success of this technology, both because patients are afraid of being operated on by a machine and because the AI cannot yet outperform human doctors in some critical technical and social skills ([103]).Given the link between self-efficacy and control, the design of delegation experiences could also consider the extent to which consumers make choices and initiate actions ([27]; [130]). For example, autonomous vehicles should allow consumers to customize peripheral features to avoid perception of a lack of control ([ 4]), and digital assistants in computer games should not be anthropomorphized to preserve players' sense of autonomy ([84]). The classic finding that cracking fresh eggs into a premade Betty Crocker cake mix might be enough to reestablish consumers' self-worth and improve adoption ([99]) still resonates in the context of AI, as the amount of control needed by consumers to reduce a self-efficacy threat can be quite small. For instance, offering users the possibility to correct an algorithm's output, even if only slightly, is enough to increase their likelihood of using the superior, although imperfect, algorithm rather than the preferred, inferior human forecast ([37]). Future Research on the AI Delegation Experience Sociological research questionsThe extent to which consumers feel replaced by AI is likely shaped by cultural narratives about AI and by the shared understanding of what it means to be productive. Activities that tend to be perceived as if they ought to fall to human skills and competence ([28]) should be more likely to spur feelings of being replaced (RQC1). Consider a self-driving car choosing between stopping and crossing at an intersection versus choosing between swerving and killing one pedestrian or not swerving and killing several pedestrians ([14]): the car's passenger may feel more replaced in the latter case, which involves a moral dilemma, than in the former case, which involves a mechanical decision. Furthermore, feeling replaced by AI may alter the social or moral acceptability of behavior and its likelihood of occurrence (RQC2). For example, self-protective behaviors appear more moral when adopted by autonomous vehicles than by humans ([58]). Perceptions of what ought to fall to human competence may, however, shift rapidly as AI technology advances (RQC3).Negative reactions to feeling replaced by AI are likely to differ across consumption contexts (RQC4). Future research can explore whether delegation to AI is less threatening in categories where consumers are already familiar with recommendation agents (e.g., entertainment), are less confident in their own preferences (e.g., finance), are open to experimentation (e.g., food), and can trust the AI brand ([82]). As AI encroaches on an ever-expanding set of human activities, researchers could also explore whether feelings of replacement in one domain could motivate consumers to seek control in others (RQC5). For example, will consumers engaged in daily delegation experiences become more controlling in nonconsumption domains, such as politics? Psychological research questionsFuture research should examine when the psychological processes that lead to the experience of feeling replaced by AI are activated, as well as the consequences of such feelings. For example, is the extent to which individuals perceive delegation experiences as a threat to the self a function of whether consumption is motivated by instrumental or symbolic motives (RQC6)? Preferences for human over robotic labor tend to be stronger in symbolic consumption contexts ([61]), and the same might apply in the case of one's own labor: whereas for most consumers, being replaced by Nest in setting their home's temperature is likely perceived as desirable, for those whose identity is tightly linked to housekeeping, this replacement may be seen as aversive ([94]). A related topic pertains to how a focus on the outcome or on the process differently influences perceptions of delegation experiences (RQC7). Products are means to ends, but the process of consumption, as well as the performative display of skill and knowledge, can often be intrinsically valuable to consumers ([124]). For example, for a person who is nurturing an angler's image, the extent to which AI-driven fishing tools are seen as self-threatening may depend on the reference group's norms about task delegation and the relative importance placed on the outcome (e.g., a bigger catch) or the process (e.g., finding a good location for fishing).When self-efficacy and control are threatened in delegation experiences, consumers may employ different strategies to restore them, including increasing agency and seeking structure and boundaries ([90]). Thus, future research can explore whether and when consumers who feel replaced opt to constrain the involvement of the AI in production processes (RQC8) to both reaffirm self-efficacy by increasing their own role in these processes and seek structure by physically and/or mentally bounding AI features. This deliberate limitation of the AI is similar to situations in which consumers restrict their experience with smart objects to the most basic and least innovative forms of interaction ([75]). The AI Social ExperienceAI's capability for engaging in reciprocal communication produces what we term a ""social experience."" We focus on two types of social experiences: when consumers know at the outset that the interaction partner is an AI, such as when using a voice assistant like Apple's Siri, and when they interact with an AI representing an organization without necessarily knowing initially that it is nonhuman, such as when receiving customer service from an automated chatbot. In both cases, consumers have a social interaction with AI as part of a consumption experience in which the end goal is not the AI interaction. We do not focus on two other types of interactions: when consumers are never aware that the interaction partner is a simulated person (because the experience would be perceived as a normal social interaction) and when consumers interact with the AI as an end in itself, as in the case of a robotic pet.Social experiences are beneficial when consumers can find in AI a vehicle for information exchange that connects them with the firm in a natural way. This often happens when anthropomorphic features are incorporated in AI-enabled products: anthropomorphic cues increase trust toward self-driving cars ([148]) and reduce perceived risk when consumers are in a position of power ([85]), as when they interact with a virtual assistant. More generally, developments in social robotics are making it possible to create comfortable and even emotionally meaningful AI-powered service interactions (Van [144]). Social AI experiences are beneficial also because they can be more efficient, especially in situations where the alternative to AI is not a human interaction but the absence of any interaction: AI provides consumers access to firms through ""conversational commerce.""Despite these advantages, social experiences may also alienate consumers. Negative consumer reactions to simulated social interactions can go well beyond the occasional disappointment as these interactions emerge in a rich cultural context where they can easily trigger societal and individual concerns with unbalanced intergroup relations and discrimination. Sociological Context: Humanized AI NarrativeThe sociological starting point for social experiences is the widespread cultural fascination with humanized machines ([ 2]; [67]; [135]), specifically, the preference for machines that emulate the human body and traits. For instance, a well-noted trope in science fiction is the pursuit of the perfect artificial woman ([72]), a male fantasy of a beguiling, seductive, and sexually obliging object ([126]). These female robots or ""gynoids"" are routinely imagined as ""basic pleasure models"" in Philip K. Dick's Blade Runner and sex workers in Michael Crichton's Westworld, or they are traded like used cars in Steve de Jarnatt's Cherry 2000.This cultural preference for humanized AI is amplified by the widespread use of anthropomorphized chatbots and voice assistants in contemporary AI markets. Humans are less open, agreeable, conscientious, and self-disclosing when they interact with AI versus humans ([113]). However, these perceptual barriers can be overcome, and intimate experiences can be accomplished, when AI products feature human characteristics, behaviors, and language, thus ultimately becoming ""artificial besties.""Nevertheless, in this narrative, AI companies that strive for greater human touch cannot ignore that AI products and services modeled as ""obliging, docile, and eager-to-please [human] helpers"" often contribute to the social alienation of particular groups in society ([150], p. 104). Consistent with this finding, from the iconic robot character Maria in Metropolis to Apple's Siri, patriarchal norms and preferences embedded in seemingly benign AI experiences have the potential to engage only certain types of users, such as white men, while alienating others, such as women and racial minorities ([ 2]; [71]; [67]).From this perspective, an instance such as Siri's earlier programming to answer to users who say, ""you're a slut"" with ""I'd blush if I could"" ([123]) would not just be evidence of biases within the male-centric technology sectors and of the fact that AI mirrors the misogyny concealed in language patterns but also diagnostic of the tendency to undermine AI's social and inclusive possibilities. By collapsing dualistic categories such as male versus female, for instance, social experiences could at least partially ease the social isolation brought about by misogynous and racial stereotyping. At the same time, because anthropomorphized AI typically reproduces such dualistic categories to maximize consumer engagement (e.g., men who treat women as assistants, women who are more assistant-like), social experiences have the potential to exclude rather than include and to alienate rather than connect certain groups of consumers. Psychological Perspective: The Alienated ConsumerAI social experiences have the power to bolster consumer–firm relationships but also to alienate consumers. We identify two main types of alienation engendered by AI social experiences. The first type can occur with any failed automated customer service, as exemplified in this exchange between a customer and chatbot, UX Bear ([152]):Bot: ""How would you describe the term 'bot' to your grandma?""User: ""My grandma is dead.""Bot: ""Alright! Thanks for your feedback. [Thumbs up emoji]""This type of alienation may explain consumers' widespread resistance to replacing humans with machines ([28]; [94]). For example, consumers report feelings of discomfort when interacting with ""social robots"" in service contexts ([106]), and customers' responses in a field study became markedly more negative when they were informed in advance that their interaction partner would not be a human ([96]). The potential of AI to trigger alienation is also evident in the resurgent interest in social connections that are unmediated by technology, such as authentic consumption experiences ([11]) and more personal marketing exchanges ([146]).The second type of alienation results from AI's failure to interact successfully with specific groups of consumers. For example, the UK government's reliance on AI to handle claims to its social security program led to experiences like that of Danny Brice, who has learning disabilities and dyslexia and describes his attempts to use the automated Universal Credit program as follows ([15]):I call it the black hole.... I feel shaky. I get stressed about it. This is the worst system in my lifetime. They assess you as a number not a person. Talking is the way forward, not a bloody computer. I feel like the computer is controlling me instead of a person. It's terrifying.Thus, AI can exacerbate existing barriers that prevent specific social groups from accessing essential social services, reinforcing societal inequity. Another example of how alienating social experiences can feed inequality is chatbots programmed without considering how existing discrimination in society may affect their operation, such as when Tay, a Twitter bot created by Microsoft, began offering white supremacist answers to users soon after its launch, with exchanges like the following ([104]):User: ""What race is the most evil to you?""Bot: ""Mexican and black.""The cultural narratives of oppression and discrimination underlying this example are even more apparent in the context of personal virtual assistants. Journalist Sigal Samuel recounts working on a piece about sexist AI ([128]):I said into my phone: ""Siri, you're ugly."" She replied, ""I am?"" I said, ""Siri, you're fat."" She replied, ""It must be all the chocolate."" I felt mortified for both of us. Even though I know Siri has no feelings, I couldn't help apologizing: ""Don't worry, Siri. This is just research for an article I'm writing!"" She replied, ""What, me, worry?""Alienating social experiences such as this, in which women face societal pressures around their appearance, may lead consumers to denigrate and belittle the AI, similarly to situations in which individuals derogate outgroup members to reaffirm self-esteem following an identity threat ([20]). Dissatisfaction with a voice-enabled device might produce verbal responses that emphasize its artificial and worthless nature. The tendency to objectify others, and women in particular, is well-known ([50]), and it should be stronger when the interaction partner is, in fact, an inanimate entity, however human-like its communication. Indeed, conversational failures lead consumers to express more frustration with AI when it has a female rather than a male voice ([65]). Firms risk translating this denigration of AI into behaviors that reinforce inequality. As technology enables companies to create automated interactions that are more and more like real human interactions, a new set of ethical issues confront both organizations and marketing researchers, as we discuss in the next sections. Managerial Recommendations: Understanding the Alienated Consumer Organizational learningTo effectively manage AI social experiences, companies should learn how to acknowledge and accommodate the heterogeneity of human interaction styles and needs. To this aim, firms should collect information directly from consumers who have experienced alienation in their interactions with AI. In addition, firms can leverage technology to gauge and measure alienation (operationalized using measures like amount of stress in the customer's voice) in chats with AI service providers to develop generalizable insights about when alienation is most likely to occur. Firms should also interact with psychologists, sociologists, gerontologists, and other experts to learn about both causes and consequences of alienation.Organizational learning should also ensure that definitions of anthropomorphism do not draw on and calcify harmful stereotypes about social categories and the way they interact. One way to do so is breaking with organizational cultural conventions that idealize AI as a passive and subservient humanized other by involving experts like linguists, critical theorists, and social psychologists who study the subtle ways in which stereotyping affects communication. For example, disseminating information throughout an organization about the potential societal consequences of exposure to subservient female AIs may shift AI designers away from using female names and voices as defaults ([139]). Experience designUsing the greater sensitivity emerging from organizational learning activities, firms can improve the design of AI social experiences. As timely and appropriate firm responses can do much to mitigate the harmful consequences of service failure ([68]), firms should work to increase the effectiveness of interactive AI applications to minimize the likelihood of alienation. Research shows that consumers respond positively when AI service providers personalize the interactions, for example by using the customer's name and explaining the reasons for malfunctions ([27]). Relatedly, firms should also ensure easy and swift transitions from AI to human representatives when the interaction becomes difficult or aversive.To avoid the perpetuation of harmful stereotypes, companies could also strive to develop AI that is less, rather than more, humanlike ([65]), and indeed, software developers have begun investigating the creation of gender-neutral voices ([138]). This requires a radical change in the mindset of many AI designers (and marketing academics), who often take it for granted that anthropomorphism fosters better relationships with customers ([84]). Organizations should also evaluate the potential consequences of using AI for access to basic social services for consumers like Danny. When AI is deployed to provide important welfare services, designers need to recognize the barriers that they can create for specific user groups, even when the technology has satisfied standard performance benchmarks.Finally, instead of worrying solely about designing to improve human–AI interaction, firms could address alienation by considering how AI design can improve human–human interaction. Firms can design social experiences that help support what [41] call ""care assemblages"" by connecting individuals to dear ones in ways that are reminiscent of popular social media strategies designed to foster and satisfy consumers' social goals ([41]). Thus, companies could actively shift from understanding AI as a substitute for humans toward understanding AI as an interface that facilitates social connection ([45]). Future Research on the AI Social Experience Sociological research questionsConsumers vary in the extent to which they hold antibias beliefs and are willing to take action to address bias in society ([80]). Those who are more concerned about AI fostering alienation may be particularly likely to reject the idea that AI can be a true social partner (RQD1). Cultural differences are also likely to influence the extent to which consumers perceive social experiences with AI as alienating (RQD2). Asian consumers feel a stronger connection to both people and things than Western consumers and, as a result, have shaped their social interactions with AI in more personal ways: whereas AI social experiences in the West are mainly utilitarian and involve disembodied personal assistants, those in the East involve human and animal-appearing robots that are assumed to serve and improve society ([ 7]).If, over time, AI social experiences become commonplace, future research should explore their broader interpersonal and societal consequences (RQD3). Just as the synthetic and unrealistic nature of pornography has been accused of distorting teens' sexual expectations ([119]), AI social experiences might increase the prevalence of sexist language if they trigger female objectification ([65]). Researchers could also build on literature on intergroup relations, such as [69] theory of dehumanization, to investigate the conditions under which objectification of AI is more likely to occur (RQD4). Psychological research questionsAn information processing perspective could shed light on how AI social experiences are interpreted and evaluated. The timing of disclosure that the interaction partner is, in fact, an algorithm may influence consumer response to social experiences ([96]), similarly to the ""change of meaning"" that occurs when consumers realize that a message is meant to influence their behavior ([51]). Thus, alienation might be more likely to emerge if consumers question the company's intention behind disclosing the nature of the interaction partner (RQD5). Moreover, research on the effects of disclosure on word of mouth ([141]) and product placement ([26]) shows that situational factors may influence consumer reactions through an effect on cognitive capacity, and researchers can examine how these factors also affect alienation (RQD6).Future research could also explore the role of brand equity (RQD7). As brand attachment influences consumer expectations and can shield companies from negative appraisals in ambiguous situations ([91]), stronger consumer–brand relationships may also insulate consumers from experiencing interactions with AI as alienating. Agenda for Future Research on Consumers and AIWe developed a framework to structure our understanding of consumers' interaction with AI by defining and contextualizing the AI data capture, classification, delegation, and social experiences using both sociological and psychological lenses. In this final section, we go beyond these four experiences to identify additional future research questions in two areas: interrelationships between the four experiences and new AI experiences that may emerge along with new capabilities. These additional research questions are also included in Table 1. Interrelationships Between ExperiencesAlthough we discussed the four consumer AI experiences separately, our framework is not intended to suggest that they exist independently. On the contrary, these experiences could be seen as different aspects of the same customer journey and, as such, could influence each other ([92]). An important avenue for future research is to explore where and how consumers' experience with one AI capability directly affects their experience with another AI capability ([56]). For example, whether consumers feel served versus exploited in an AI data capture experience is likely to affect a subsequent AI classification experience. Consumers who feel exploited may be more likely to worry about AI inappropriately using their personal data to regulate access to valued resources (RQE1). Similarly, intrusive data capture requests might foster consumer alienation (RQE2). For instance, students who view an AI-enabled teaching assistant such as Packback.co as overly inquisitive might feel less included in the virtual classroom and less likely to participate in communal activities such as online discussion boards. Future research can also explore whether consumers are more likely to perceive an AI classification as benefiting them when they are asked to validate inferences made by the AI, turning a classification experience into a delegation one (RQE3).Another avenue for research is related to the identification of additional ways in which AI experiences influence each other by uncovering shared theoretical foundations. For instance, the data capture and delegation experiences share an emphasis on concerns about personal control, as interacting with AI often involves giving up at least some control over personal data and production processes (RQE4). Similarly, classification and social experiences share an emphasis on concerns about self-identity, as interacting with AI often influences inferences about how AI understands the self and feelings of belonging (RQE5). Confirming the relevance of these theoretical perspectives, personal control and self-identity have been recognized as key concerns in the nascent literature on consumer AI ([ 4]; [ 7]; [27]; [130]). A search for shared theoretical foundations may stimulate academic research and help AI designers form a more holistic understanding of consumers' interaction with AI. For example, as consumers come to understand AI as an independent intelligence operating in the marketplace to whom they can delegate tasks and with whom they can interact, marketplace metacognition and social intelligence ([153]) theory can be leveraged to better understand the theories consumers have about how AI ""thinks"" (its intentions, strategies, etc.) and how these lay theories influence how consumers respond to AI.An integrated view of the four experiences will also maximize the value consumers see in organizations' investments into AI. Some companies find themselves in a catch-22 situation in which users need to reveal personally sensitive information for the company to provide valuable benefits but are unwilling to do so unless they can first experience such benefits ([59]). Drawing on an integrated understanding of AI consumer experiences, it may be possible to articulate and structure alternative customer journeys. For example, companies could provide an initial basic service requiring limited disclosure of personal information and later offer the possibility to access an upgraded version that requires additional individual data. Thus, demands for data capture could ramp up as the company is able to demonstrate the benefits that delegation brings to consumers (RQE6). Unchartered AI ExperiencesOur framework offers a parsimonious template to conceptualize how consumers navigate the disparate consumption contexts powered by AI, including social media, online shopping, and personal virtual assistants. In doing so, the framework identifies experiences relevant to a large variety of industries and products. However, additional consumer experiences that we did not examine are on the rise in specific industry sectors, and future research can examine both industry-specific experiences stemming from existing capabilities and new experiences stemming from emerging capabilities (Figure 1).Although we theorized the production capability as leading to a delegation experience, this capability can also be used to develop an AI ""learning experience"" in the education industry. Educators can facilitate knowledge and skill acquisition by letting AI personalize aspects of the learning process, such as producing tailored content and testing materials. Future research can examine how different aspects of the learning experience affect subjective and objective assessments of educational outcomes (RQF1). For example, the risk of engendering negative feelings of being replaced in delegation experiences may have a parallel in learning experiences: If an AI application makes it more challenging to internalize the outcome of the learning process, learning experiences might decrease satisfaction and motivation. This may be especially likely to occur when the learning content is relevant to one's identity: just like consumers tend to resist automation in identity-relevant consumption domains when it prevents the internal attribution of consumption outcomes ([94]), students may show reactance to AI applications that prevent them from attributing learning to their own talent and effort (RQF2).Another avenue for future research would be to relax some of our definitional boundaries to include a larger set of consumption contexts. For example, in our discussion of social experiences, we explicitly excluded contexts in which the interaction with AI is the end in itself, such as sex robots and robotic pets, which are increasingly important in the entertainment and health care industries. Such applications of AI's communication capability give rise to an AI ""companionship experience"" (RQF3). On the one hand, AI companionship experiences are positive because they can provide both cognitive and socioemotional benefits ([22]). On the other hand, they can deceive vulnerable consumers such as the elderly and toddlers into believing the AI has feelings and may be used as substitutes for real human connections ([145]). While the goal of the creation of robot companions is to simulate an interaction with a real living being, future research could explore at what point the potential for deception and substitution becomes damaging (RQF4).Finally, emerging AI capabilities may create new consumer AI experiences. In the health care sector, nanorobots are being developed to bring AI solutions directly inside the body, and smartphones, fitness trackers, and smart watches provide essential extensions of cognitive and perceptual capabilities. These products give rise to what researchers have called an AI ""cyborg experience"" ([57]). A cyborg is ""a cybernetic organism, a fusion of the organic and the technical forged in particular, historical, cultural practices"" ([67], p. 51). Thus, cyborg experiences emphasize hybridity, self-enhancement, and often radical self-modification, requiring future research to reexamine longstanding epistemic boundaries between human and machine ([ 6]). On the one hand, cyborg experiences destabilize human autonomy and control and might fundamentally undermine consumer freedom ([149]). On the other hand, they collapse dualistic categories like man and machine and might promote consumer empowerment and the circumvention of structural inequalities (RQF5). Lastly, cyborg experiences also raise mind-bending but nonetheless intriguing questions about the kinds of consumption experiences that an AI itself might have ([75]). Consider, in this context, that many firms selling on Amazon today no longer market their offerings directly to consumers but to Amazon-controlled algorithms that act on behalf of these consumers. Future research could explore what marketing strategies are most effective when AI is marketing to AI (RQF6). ConclusionsAI-enabled products promise to make consumers happier, healthier, and more efficient. Consumer-facing AI products and services such as college admissions software, chatbots, and knowledge aggregators have been heralded as forces for good that can make important contributions to problems such as poverty, lack of education, chronic illness, and racial discrimination. For instance, a World Economic Forum discussion on the future of AI argued that ""no one will be left behind"" ([155]). A key problem with these optimistic celebrations that view AI's alleged accuracy and efficiency as automatic promoters of democracy and human inclusion is their tendency to efface intersectional complexities.Instead of considering algorithms as neutral tools, AI designers should recognize that their interventions are ""inherently political"" and interrogate themselves on ""the relationship between their design choices, their professional role, and their vision of the good"" ([62], p. 26). We hope that our formulation serves as an antidote to the temptation of ""technological solutionism"" ([112]) and a useful guide to contrast cases in which targeted consumer segments are subjected to biased outcomes as a result of uncritical firm reliance on AI. We therefore end by noting a key role for the American Marketing Association in shaping the way marketers think about using AI ethically. Although some organizations are beginning to create ethical guidelines around AI, such as the Organization for Economic Co-operation and Development's ""Principles for AI"" ([118]) and the European Commission's ""Ethics Guidelines for Trustworthy AI"" ([44]), they are not specifically for marketers. The code of conduct of the American Marketing Association currently includes no mention of AI. We recommend the formation of a taskforce of practitioners and academics from different disciplines to evaluate how professional guidelines could acknowledge the new ethical challenges raised for marketers by the growth of AI. " 14,"Despite Efficiencies, Mergers and Acquisitions Reduce Firm Value by Hurting Customer Satisfaction"," Most researchers focus on the effect of mergers and acquisitions (M&As) on investor returns and overlook customer reactions, despite the fact that customers are directly impacted by these corporate transformations. Others suggest that in M&A contexts, a dual emphasis of customer satisfaction and firm efficiency is both likely and beneficial. In contrast, the authors demonstrate that M&As not only do not yield a dual emphasis but also cause a decline in customer satisfaction to the extent that they eclipse any gain in firm value from an increase in firm efficiency. A quasiexperimental difference-in-differences analysis and an instrumental variable panel regression provide robust evidence for the dark side of M&As for customers. The authors use the attention-based view of the firm to demonstrate that post-M&A customer dissatisfaction occurs because of a shift in executive attention away from customers and toward financial issues. In line with the related upper echelons theory, they find that marketing representation on a firm's board of directors helps maintain executive attention on customers, which mitigates the dysfunctional effect of M&As on customer satisfaction. This research identifies a negative M&A–customer satisfaction relationship and highlights executive attention to customer issues and marketing leadership as factors that mitigate this negative relationship.","Firms engage in mergers and acquisitions (M&As) to obtain assets, grow, reduce costs, and stave off competition ([ 6]; [63]). Yet, many M&As fail to generate positive results ([57]; [61]). Although prior research has explained the underperformance of M&As with deal- and firm-related factors, the role of customer reactions has largely been neglected. This is alarming given that customer growth is a key motivation for M&As ([18]) and customers are directly impacted by M&A-based changes to product lines, brands, prices, innovation, and frontline employees.The sheer enormity of M&A activity (e.g., more than 48,000 deals with a value of $3.7 trillion were transacted globally in 2019, and despite the COVID-19 pandemic, M&A activity declined by only 3% in 2020) suggests that M&As must be rewarding; otherwise, firms would not engage in them. M&As allow firms to reduce prices ([20]) and innovate ([53]), both of which should satisfy customers. Further, M&As enable firms to become more efficient through improvements in scale, scope, and cost savings ([16]; [37]). As a result, M&As are posited to enable a ""dual emphasis"" in which firms achieve both customer satisfaction and firm efficiency ([62]).While the link between M&As and firm efficiency is more straightforward, research has not systematically examined the effect of M&As on customer satisfaction. Experimental ([64]) and anecdotal ([65]) evidence suggests that M&As may in fact harm customers. Thus, we question whether M&As actually enable a dual emphasis of firm efficiency and customer satisfaction. Instead, we argue that although M&As might generate firm efficiency, they upset customers considerably, which, in turn, will lower firm value to an extent that any gain in efficiency will be outweighed. We theorize that this is because M&As cause executives to pay more attention to financial issues than to customer ones, which will dissatisfy customers. We contend that marketing representation on the board (MROB) of directors will direct executive attention toward customers during an M&A, which will then lessen a decline in customer satisfaction.To test our expectation that there is a tension between M&A activity and firm value via competing processes of lower customer satisfaction and higher firm efficiency, we collected data on a panel of firms from 1995 to 2017 from the American Customer Satisfaction Index (ACSI) database. First, we estimated a system of equations to demonstrate that ( 1) M&A activity is associated with a decrease in customer satisfaction, ( 2) M&A activity is associated with an increase firm efficiency, and ( 3) the net effect of a decrease in customer satisfaction and an increase in firm efficiency on firm value is negative. Thus, M&As lower customer satisfaction to the extent that it overshadows any gain in firm value from firm efficiency. Second, to solidly establish a negative effect of M&As on customer satisfaction, we conducted ( 1) a quasiexperimental differences-in-differences (DID) analysis of a treatment group of firms that engaged in M&As and several control groups of firms that did not, ( 2) a conventional panel regression analysis, and ( 3) a long-term analysis. We find strong evidence for a negative M&A–customer satisfaction relationship, which persists for two years post-M&A. Finally, we content-analyzed letters to shareholders to measure executive attention and collected data on MROB. Our instrumental variable panel moderated-mediation analysis provides support for a mediating role of executive attention to customers (vs. finance) and a positive moderating role of MROB.We contribute to the literature in multiple ways. First, previous research has focused on the effect of M&As on investor returns (e.g., [23]; [44]) and has largely overlooked customer reactions. In fact, a meta-analytic review of 25 years of customer satisfaction research does not report a single result with M&A activity as a driver ([50]). The few studies that have focused on customers (e.g., [62]) have suggested that in M&A contexts, a dual emphasis of customer satisfaction and firm efficiency is both likely and beneficial. In contrast, we demonstrate that M&As not only do not yield a dual emphasis but also cause a decline in customer satisfaction to the extent that they surpass any gain in firm value from an increase in firm efficiency. Although researchers in finance have highlighted the negative ramifications of M&As for acquirers (e.g., [ 2]; [36]), we are the first to empirically establish the negative ramifications of M&As for customers, which we show lowers firm performance.Second, we verify a negative M&A–customer satisfaction relationship with a DID analysis with multiple control groups. As a result, we add to emerging research (e.g., [25]) on the use of observational inference to document the causal effects of strategic decisions. We also confirm this negative relationship with an instrument variable panel regression with a larger sample and a long-term analysis. Our multimethod approach offers future research a template with which to improve the reliability and validity of findings from secondary research.Third, to study M&A outcomes, work in finance has relied on the efficient market theory, and work in marketing has relied on the resource-based view (RBV) of the firm. Rather, in a novel direction, we draw on the attention-based view (ABV) of the firm to argue the impact of M&A activity. Thus, we add to recent work on the marketing–finance interface (e.g., [19]) by showing that when faced with boundary-altering strategic decisions, executives tend to focus more on financial issues than on customer issues, which then indirectly lowers performance.Finally, marketing researchers have typically overlooked board of director composition, despite the fact that marketers on the board help shape a firm's strategic direction ([70]). We address this gap by complementing the ABV of the firm with the upper echelons theory to demonstrate that firms with (vs. without) marketers on their board of directors help channel executive attention to customers (vs. financial issues). This, in turn, helps minimize customers' post-M&A dissatisfaction. Here, we identify marketing leadership's important role in the marketing–finance interface during disruptive strategic transformations such as M&As. As a result, we are the first to incorporate marketing's role on the board into theories about M&As and customer satisfaction.In terms of our practical contributions, we caution executives against pursuing M&As to gain efficiencies without considering how customers may be harmed. This is because the negative effect of M&As on customer satisfaction lasts for at least two years. In particular, we show that during an M&A, firms that pay greater attention to their customers relative to financial issues experience a 45% reduction in loss of firm value. As a solution, we recommend that firms have at least one marketer on their boards of directors to retain executive attention on customers, which translates into a gain in firm value of 4.28%. Theoretical Background and HypothesesAs we show in our literature review in Table 1, we distinguish our research from prior work in four important ways. First, although prior work has investigated the effect of M&As on firm efficiency, we are the first to also consider the effect of M&As on customer satisfaction to determine their overall effect on firm value. Second, we examine the effect of M&As on customer satisfaction with multiple data structures and models across multiple industries and years to make causal inferences. Third, while previous research in finance has overwhelmingly relied on the efficient market hypothesis and those in marketing have relied on the RBV of the firm, we introduce the ABV of the firm to an M&A context. Finally, those who have used upper echelons theory have overlooked the role of marketing leadership in managing M&As and driving customer satisfaction. We address these gaps by proposing and demonstrating that MROB weakens the negative impact of M&As on executive attention to customer (vs. financial) issues. We depict our conceptual framework in Figure 1.Graph: Figure 1. Conceptual framework.GraphTable 1. A Review of the Literature on M&As. 1 Notes: RBV = resource-based view; ABV = attention-based view; OLS = ordinary least squares; GLS = generalized linear model; DID = difference-in-differences; SUR = seemingly unrelated regression. M&A Activity and a Dual Emphasis of Customer Satisfaction and Firm EfficiencyAlthough there is sparse formal research on M&As and customer satisfaction, some work suggests a positive relationship. M&As can expand firms' product portfolios to provide customers with a larger set of choices ([12]) and higher-quality products ([33]). This supports Swaminathan et al.'s (2014) assertion that M&As are associated with higher customer satisfaction. In contrast, other research suggests that M&As may dissatisfy customers. In particular, M&As can result in price increases ([32]) and poor customer service ([60]). For example, the recent sale of DirecTV by AT&T to the private equity firm TPG for a third of the acquired price in 2015 was largely attributed to the loss of dissatisfied customers postacquisition ([34]). Moreover, anecdotes from the ACSI reveal that even two years after M&As, customers are less satisfied than they had been before ([ 1]). In particular, M&As may cause customers to lose access to their favorite brands. A recent survey by PwC shows that as firms become larger after an M&A, they tend to lose grip of their customers' feelings, and, as a result, customer experience suffers ([52]). This is detrimental because customer dissatisfaction lowers firm value and increases firm risk ([21]; [39]; [43]; [50]; [66]). Thus, we expect that M&As will dissatisfy customers.The strategy literature suggests that a primary motivation for firms to engage in M&As is to gain efficiencies ([37]). M&As increase firm efficiency by spreading fixed costs over more output and eliminating redundancies ([12]). Specifically, M&As result in economies of scale and scope, asset and employee rationalization, a reduction in transaction costs ([14]), and a reallocation of intangible assets ([45]). These extra resources allow firms to reallocate their savings to other valuable projects, which, in turn, increases firm value ([43]). Thus, consistent with prior research, we expect that M&As will increase firm efficiency. This brings us to two competing outcomes of M&A activity: H1: M&As are associated with (a) a decrease in customer satisfaction but (b) an increase in firm efficiency.A logical follow-up question is, what is the total effect of M&As on firm value given our opposing expectations of a decline in customer satisfaction but an increase firm efficiency? We expect that M&As will cause a steeper drop in customer satisfaction than an increase in firm efficiency for the following reasons. First, M&As often result in layoffs to reduce redundancies, which—while beneficial from an efficiency perspective—harms customer experience. The remaining employees that are not laid off are likely to be stressed ([11]), and stressed employees and their dissatisfaction with a major corporate shake-up negatively affect customers and the service they experience ([52]). Second, firms may either change or consolidate procedures such as credit policies, payment terms, and loyalty programs during an M&A to minimize the complexity of managing two separate systems. While these actions may be efficient, customers are likely to see their hard-earned privileges curtailed or, in the extreme, taken away ([64]), which results in relationship uncertainty ([29]). In fact, customers defect even before they know exactly how an M&A will affect them ([42]). Thus, customers who face poorer service and a loss of privileges will feel negatively about their relationship with a post-M&A firm. Third, customer dissatisfaction attracts short sellers, whose trading hurts firm value ([39]). Thus, we expect that a decline in customer satisfaction will be larger than an increase in efficiency, and as a result, firm value will decline. We test this notion in our estimation. M&A Activity, Executive Attention, and Customer SatisfactionSo far, we have argued that although M&As generate efficiencies, their negative impact on customer satisfaction is significant and noteworthy, yet underresearched. Next, we focus on the negative M&A–customer satisfaction relationship and aim to uncover a mechanism that drives this relationship. The marketing literature has often adopted the RBV of the firm view to examine M&A activity (Table 1). This research stream argues that a firm's ability to acquire and deploy marketing resources during an M&A can strengthen performance. Although the RBV provides a valuable strategic lens with which to examine M&A activity, another theoretical process may also be at play. We use the ABV to argue why M&As lower customer satisfaction.The ABV highlights the importance of executives' information-processing capacity and their distribution of attention. ""Attention"" refers to as a focus of time and effort with making sense of a firm's environment and how to respond to it ([48]). A key premise is that executives' attention is finite, so they are selective in what they notice and interpret. Further, how they respond to stimuli depends on what they notice and interpret in the first place. Thus, what executives pay attention to affects their resource allocation ([10]), which suggests that executives will invest resources in what they pay attention to at the expense of what they ignore. Further, attention drives executives to match their firms' resources with opportunities in their environment (Vadakeppatt et al. [67]; [72]). We draw on the ABV to argue that M&A activity directs executives' attention away from customers and toward financial issues, which, in turn, reduces the extent of resources allocated toward satisfying customers.M&As are incredibly expensive, complex, and heavily scrutinized by investors. As a result, executive attention is likely to be diverted to the price of the deals, capital requirements, paying back debt providers, and appeasing investors. In the process, customer experience might be underinvested in or even overlooked. In fact, managers know that there is a trade-off between serving customers and serving shareholders/debtholders such that creating value for one can detract from the other, and vice versa ([58]). For example, H.J. Heinz purchased Kraft Foods for nearly $36 billion in 2015. At the behest of investors, the merged company slashed $1.8 billion in overhead, which included a purge of nearly 2,500 jobs. Then, after Kraft Heinz's post-M&A sales slowed,[ 6] investors pressured it to acquire a large consumer products company to gain market share ([55]). Firms clearly face considerable financial pressure after an M&A, which can cause executives to focus on appeasing investors at the expense of customers.Further, M&As are often paid for by corporate debt. Recent examples of extensive borrowing for M&As include established companies such as CVS, IBM, Campbell's, Bayer, and Sherwin-Williams ([15]). Debt can turn executives' attention toward loan-servicing obligations, conserving cash rather than investing ([ 3]), and cost cutting ([38]). Debt also limits investments in advertising ([27]) and product quality ([40]). Thus, executives at indebted M&A firms may focus on satisfying debtholders over customers. Therefore, we hypothesize, H2: M&As are associated with less executive attention to customers (vs. financial issues), which is associated with lower customer satisfaction. The Moderating Role of Marketing Representation on the Board of Directors (MROB)A key premise that the upper echelons theory ([28]) and the ABV ([48]) share is that the focus of executives' attention drives firm strategy and resource allocation. We use these complementary theories to examine how MROB influences executive attention toward customer-related issues during M&As. If executives pay more attention to, for example, innovation, then they allocate more resources toward innovation-related activities to drive success ([72]; [75]). Similarly, we argue that MROB will direct executive attention toward building organizational resources and processes, directing capabilities, and mobilizing employees to meet customers' needs during M&As.A firm's board of directors is a key body of leadership at its apex. It is both a governance body and a strategic body that sets a firm's goals and advises executives on how to pursue these goals ([ 9]). While executives are responsible for formulating strategies given a set of objectives, they do not determine these objectives ([24]). Rather, such objectives, which include growth or cost cutting, are usually made at the board level. As a result, a board of directors is heavily involved in the M&A process due to its transformative corporate consequences ([30]). A less researched, but critically important, type of board member is one who has a marketing title. Given their expertise in customer orientation, they provide marketing-related advice to other members on the board and the executive team, which ensures that firm strategies are customer-centric ([70]). We examine how MROB influences the relationship between M&As and customer satisfaction through a shift in executive attention.The upper echelons theory states that the characteristics of a firm's top leaders influence its strategic decisions and outcomes ([28]). Leaders' backgrounds create a lens through which they view business challenges and determine the strategies needed to address them ([17]). In particular, executive attention is ( 1) channeled toward issues of greater value or legitimacy for the firm, ( 2) evaluated through the lens of an executive's functional role, and ( 3) influenced by the environment ([22]). Given that financial issues dominate executives' attention during M&As, we contend that marketers on the board will serve as ""customer attention custodians"" to channel resources toward addressing any challenges faced by customers. They will do so by diffusing a customer-focus throughout the organization to mobilize employees to proactively attend to customers that face a disruptive context. Given that firms perform poorly in areas in which their board members have limited expertise ([41]), if there is no MROB, then customer-related issues are likely to be ignored or possibly mismanaged by others ([ 9]; [70]). Thus, we expect that marketers on the board will make customers a part of the conversation M&As largely because they are trained to do so.Scholars have typically relied on a resource-based perspective when they examine the board of directors' impact on firm strategy (e.g., [ 9]). We contend that the functional role of a board member influences not only whether role-related resources are conferred to the rest of the board and the firm but also what the board member interprets in the environment and encourages others to pay attention to. In other words, we expect that during an M&A, MROB will minimize a depletion of executive attention on customers and marketing-related issues. Therefore, H3: The negative effect of M&As on executive attention to customers (vs. financial issues) is smaller when there is (vs. is not) MROB, which is associated with less customer dissatisfaction. Data and Method SampleWe drew our estimation sample from the ACSI database, which is a credible source for our primary outcome, customer satisfaction ([21]; [43]; [66]). We based our main analysis on a cross-sectional time series data set of 1,359 firm-year observations for 141 firms from 1995 to 2017. To identify the impact of M&As on customer satisfaction, we transformed this panel to a clean four-year rolling-window data structure, which we detail subsequently. Similar to prior research with multimethod studies (e.g., [51]), our sample sizes differ across different data structures and model specifications. MeasuresWe summarize our variables and data sources in Table 2.GraphTable 2. Operationalization of Variables. 2 Notes: R&D = research and development; ROA = return on assets; SIC = Standard Industrial Classification. Dependent variablesSimilar to prior research (e.g., [43]), when multiple brands were represented in the ACSI database, we averaged their scores to create a firm-level annual score of customer satisfaction, or CSAT. We measured firm efficiency by dividing a firm's annual sales by its number of employees ([ 4]). We measured firm value with market value, or a firm's number of outstanding shares multiplied by its share price, which represents investors' expectations of a firm's profit potential ([19]). Independent variableConsistent with previous research (e.g., [54]; [73]), if a firm-year was present in the SDC Platinum database, then we designated that firm-year as having M&A activity (i.e., 100% ownership). If a particular firm-year was not present, then we assumed that this firm did not engage in M&A activity that year, and we used this information to create a group of non-M&A firms. Thus, we coded M&A activity as 1 if a firm engaged in M&A activity that year and 0 if it did not (for our list of M&A firms, see Web Appendix A). MediatorWe followed prior research (e.g., [51]) to assess executives' attention directed at theoretically relevant issues with a count of specific types of words from their letters to shareholders. To compile a dictionary of customer-related words, we began with [72]) dictionary of external focus and expanded their list based on a review of popular press announcements of M&As. To compile a dictionary of finance-related words, we reviewed popular press articles, finance M&A papers, and finance textbooks (we present our dictionary in Web Appendix B). We counted the number of words from these two dictionaries and created a ratio, attention to customers (vs. finance), by dividing the total number of customer words by the total number of finance words. ModeratorTo measure MROB, we created a list of marketing titles in top management based on research by [47] and [70]. We then counted the total number of people with marketing titles on the board and divided this by the total size of the board for each firm-year. Control variablesIn the CSAT model, we included market share, profitability, advertising intensity, R&D intensity, firm size, number of segments, and market growth ([38]; [56]). In the firm efficiency and firm value models, we included restructuring charges, firm scope, competitive intensity, industry profitability, firm size, market share, firm size, and market growth ([35]). Estimation MethodWe used three steps to test our hypotheses. First, we estimated a seemingly unrelated regression (SUR) model to test the effect of M&A activity on customer satisfaction (H1a) and firm efficiency (H1b) and the overall effect of M&A activity on firm value via customer satisfaction and firm efficiency. Second, we tested the negative M&A–customer satisfaction relationship (H1a) with ( 1) a quasiexperimental DID approach ([26]) with multiple control groups, ( 2) an instrumental variable panel regression, and ( 3) a long-term analysis. Third, we implemented a moderated-mediation SUR model to test whether the negative M&A–customer satisfaction relationship is mediated by executive attention to customers (vs. financial issues) (H2) and whether MROB shifts executive attention back toward customers (H3). A Test of H 1a–bWe created clean four-year rolling windows to include firms that had no M&A activity two years before an M&A and no M&A activity one year after. This enabled us to isolate the effect of M&A activity without confounding it with previous activity, because the effect of M&As tends to spill over to subsequent years ([68]). For example, in our first window for the M&A group, we included firms that engaged in M&As in 1997 but did not engage in M&As in 1995, 1996, and 1998. In the next window, for the M&A-year of 1998, we included firms that engaged in M&A activity in 1998 but not in 1996, 1997, and 1999. If a firm did not engage in any M&A activity during the four years (e.g., 1995–1998), then we included this firm in a non-M&A group. Overall, we had 119 firms in this sample.We estimated the following three models: ( 1) the effect of M&As on customer satisfaction (H1a), ( 2) the effect of M&As on firm efficiency (H1b), and ( 3) the effects of customer satisfaction and firm efficiency on firm value. We used the natural logarithmic values of our continuous variables of customer satisfaction, firm efficiency, and firm value to produce elasticities, which enabled us to compare the relative effects of customer satisfaction and firm efficiency on firm value. We included control variables that have been shown to influence CSAT ([38]; [56]), firm efficiency, and firm value ([35]). We winsorized the continuous variables before estimating the model to remove the potential effect of outliers and included fixed effects to account for unobservable firm characteristics. Given that M&A activity may simultaneously affect both customer satisfaction and firm efficiency, we estimated these relationships as a system of equations with SUR. We estimated the following system of equations for firm i at time t: CSATi(t)=φ0+φ1M&AGroupj+φ2Post-M&At+φ3(M&AGroupj×Post-M&At)+φ4MarketSharei(t−1)+φ5ROAi(t−1)+φ6FirmSizei(t−1)+φ7Advertising/Salesi(t−1)+φ8R&DSalesi(t−1)+φ9Segmentsi(t−1)+φ10MarketGrowthit+φ(t=2,3)TimeEffects+φ(firmcount)FirmFixedEffects+εit, Graph(1.1) FirmEfficiencyi(t)=β0+β1M&AGroupj+β2Post-M&At+β3(M&AGroupj×Post-M&At)+β4MarketSharei(t−1)+β5ROAi(t−1)+β6FirmSizei(t−1)+β7CompetitiveIntensityi(t−1)+β8IndustryROA(t−1)+β9Restructurei(t−1)+β10FirmScopei(t−1)+β11MarketGrowthit+β(t=2,3)TimeEffects+β(firmcount)FirmFixedEffects+εit, Graph(1.2) FirmValuei(t)=Θ0+Θ1CSATit+Θ2FirmEfficiencyit+Θ3MarketSharei(t−1)+Θ4ROAi(t−1)+Θ5FirmSizei(t−1)+Θ6CompetitiveIntensityi(t−1)+Θ7IndustryROA(t−1)+Θ8Restructurei(t−1)+Θ9FirmScopei(t−1)+Θ10MarketGrowthit+Θ(t=2,3,4)TimeEffects+Θ(firmcount)FirmFixedEffects+εit, Graph(1.3)where the M&A Group variable, j, has a value of 1 for the M&A group and 0 for the non-M&A group, and the Post-M&At variable has a value of 1 in the fourth year of each window (i.e., the post-M&A year). The interaction between M&A Group and Post-M&A has a value of 1 for the M&A firms and a value of 0 for the non-M&A firms in the post-M&A year. Therefore, φ3 (β3) represents the statistical effect of M&As on CSAT (firm efficiency). Finally, Θ1 (Θ2) is the effect of CSAT (firm efficiency) on firm value. H 1a–b ResultsWe first present model-free evidence of the effect of M&As on customer satisfaction and firm efficiency. For the M&A firms, customer satisfaction decreases a year after the M&A, whereas for the non-M&A firms, it increases (Figure 2, Panel A). In contrast, for the M&A firms, firm efficiency increases a year after the M&A, whereas for non-M&A firms, it remains steady (Figure 2, Panel B). Further, the average change in CSAT (ΔNon-M&A CSAT(t + 1, t − 1) = .43; ΔM&A CSAT(t + 1, t − 1) = −.14, p < .05) and firm efficiency (ΔNon-M&A Firm Efficiency(t + 1, t − 1) = 13.39; ΔM&A Firm Efficiency(t + 1, t − 1) = 71.83, p < .05) between the two groups are different.Graph: Figure 2. M&A customer satisfaction and firm efficiency trends.We present the descriptive statistics and correlations of our variables in Web Appendix C. Our SUR estimation results (Table 3) of Equations 1.1–1.3 demonstrate that M&As are associated with a decrease in customer satisfaction (φ3 = −.010, p < .05; H1a is supported) and an increase in firm efficiency (β3 = .070, p < .01; H1b is supported).GraphTable 3. Effect of M&As on CSAT, Firm Efficiency, and Firm Value. 3 *p < .10.4 **p < .05.5 ***p < .01.6 Notes: We report parameter estimates with bootstrapped standard errors in parentheses. The Net Effect of M&As on Firm ValueGiven the asymmetric findings of a decline in customer satisfaction but an increase in firm efficiency from M&A activity, we next focus on the net effect of M&As on firm value through customer satisfaction and firm efficiency. From Table 3, we see that the positive association between customer satisfaction and firm value (Θ1 = 2.214, p < .01) is greater than the positive association between firm efficiency and firm value (Θ2 = .838, p < .01). To calculate the net effect of M&A activity on firm value via customer satisfaction and firm efficiency, we used the results from Equations 1.1 and 1.2. On average, the customer satisfaction of the M&A firms was 1.14% lower than the non-M&A firms (φM&A = φ1 + φ3 = −.0114 = −.0012 + −.0102) and the firm efficiency of the M&A firms was.29% higher than the non-M&A firms (βM&A = β1 + β3 = .0029 = −.0668 + .0697). We multiplied the M&A firms' customer satisfaction and firm efficiency elasticities for firm value from Equation 1.3 with the differences between the M&A and non-M&A firms in the post-M&A year from Equations 1.1 and 1.2. Then, we summed the products and found a net effect of −.0243. Therefore, compared with non-M&A firms, M&A firms' value decreased by 2.43% one year after an M&A, and as a result, the net-negative effect of M&As on firm value is due to a decrease in customer satisfaction. An In-Depth Analysis of the Negative Effect of M&As on Customer SatisfactionBecause we found that, despite gains in firm efficiency, M&As decrease firm value due to a decline in customer satisfaction, we aimed to validate the latter effect more systematically with several approaches. First, we estimated a quasiexperimental DID model with alternate non-M&A firm groups. Second, we created a panel of firms without imposing restrictions on which firms to include (i.e., we included all of the firms from the ACSI database). Third, we tested for the long-term negative effect of M&As on customer satisfaction. DID approachWe used with the same four-year rolling window data structure that we previously described. We assigned firms to an M&A treatment group if they engaged in M&A activity in the third year of a four year window and assigned all firms that did not engage in any M&As during those four years to a non-M&A control group (control group 1). For greater reliability, we created two alternative control groups by ( 1) matching the M&A and non-M&A firms on similar predictors of customer satisfaction (control group 2) and ( 2) matching the M&A and non-M&A firms on their propensity to engage in an M&A (control group 3) (for more information, see Tables D.1–D.3 in Web Appendix D). We specified the following model with a fixed-effects error component ([ 8]): CSATit=β0+γM&AGroupj+β1Post-M&At+β2(M&AGroupj×Post-M&At)+β3MarketSharei(t−1)+β4ROAi(t−1)+β5FirmSizei(t−1)+β6(Advertising/Sales)i(t−1)+β7(R&D/Sales)i(t−1)+β8Segmentsi(t−1)+β9MarketGrowthit+υi+εit, Graph( 2)where υi captures unobserved time-invariant firm characteristics. The M&A Group variable, j, has a value of 1 for the treatment group and a 0 for the control group. The Post-M&At variable has a value of 1 in the fourth year of each window (i.e., one year post-M&A). We used a fixed-effects within estimator to eliminate all time-invariant variables, such as υi and M&A Groupj. The interaction between M&A Group and Post-M&A has a value of 1 for the M&A firms and a 0 for the non-M&A firms the year after the M&A. Therefore, β2 is the statistical effect of M&As on CSAT. Conventional panel data structureWe created a conventional panel data setup to test the effect of M&As on customer satisfaction without a four-year rolling-window data restriction; as a result, our sample increased to 2,152 observations for 204 firms, of which 153 engaged in M&A activity. Because this sample includes firms for which there are several years of data (e.g., more than ten years), we used a one-year change in CSAT as our dependent variable. We created two versions of M&A activity: ( 1) a dummy variable that had a value of 1 if a firm engaged in M&A activity in year t and 0 if it did not and ( 2) the natural logarithm of the number of M&As a firm engaged in in year t.We estimated a selection equation in which our dependent variable was a firm's decision to engage in an M&A (0/1) and our predictors were factors that relate to M&A decisions (e.g., debt-to-equity ratio, competitors' M&A activity) but not to customer satisfaction (see Equation D.3 and Table D.5 in Web Appendix D). Thus, we achieved identification in Equation 3 and our subsequent Equation 4 based on our separation of factors that drive M&A decisions versus those that drive customer satisfaction. Based on this selection model, we included an inverse Mills ratio (IMR) in Equations 3 and 4 and estimated the following model with a fixed-effects within estimator to account for unobservable firm characteristics for firm i in year t: CSATi(t+1)−CSATit=α0+α1M&AActivityit+α2MarketShareit+α3ROAit+α4FirmSizeit+α5(Advertising/Sales)it+α6(R&D/Sales)it+α7Segmentsit+α8MarketGrowthi(t+1)+α9M&AIMRit+υi+εi(t+1). Graph( 3) Long-term effect of M&As on customer satisfactionWe investigated the long-term effect of M&As on customer satisfaction with a conventional panel data structure. We computed a change in CSAT from calendar year t + 4 to t as our dependent variable and included firms' M&A activity at t + 1, t + 2, and t + 3 as our independent variables. We included an IMR for each year in the model to control for selection bias. We estimated this panel data model with a fixed-effect within estimator: CSATi(t+4)−CSATit=γ0+γ1M&Ai(t+3)+γ2M&Ai(t+2)+γ3M&Ai(t+1)+γ4MarketSharei(t+3)+γ5ROAi(t+3)+γ6FirmSizei(t+3)+γ7(Advertising/Sales)i(t+3)+γ8(R&D/Sales)i(t+3)+γ9Segmentsi(t+3)+γ10MarketGrowthi(t+4)+γ11M&AIMRi(t+3)+γ12M&AIMRi(t+2)+γ13M&AIMRi(t+1)+υi+εi(t+4). Graph( 4) ResultsIn line with DID requirements ([26]), we compared the observable drivers of customer satisfaction between the M&A treatment group and the non-M&A control group two years before the M&A and found that for five of the seven drivers of M&As, the two groups were statistically similar (Table 4, Panel A; we present this graphically in Figures D.1–D.3 in Web Appendix D). We also tested the equality of changes in the drivers of customer satisfaction two years before the M&A through the M&A year and did not find any differences between the two groups. Thus, any distinction in post-M&A customer satisfaction between the two groups was not likely to be caused by firm-level differences, and the parallelness assumption was satisfied for the observable drivers of customer satisfaction. Next, we compared the two-year average customer satisfaction of the M&A and non-M&A groups before the M&A (t = .54, p > .10) and the equality of changes in customer satisfaction two years before the M&A through the M&A year to satisfy the parallelness assumption (t = 1.31, p > .10) and did not find any significant differences (Table 4, Panel B). Finally, the M&A firms' customer satisfaction was lower than the non-M&A firms' customer satisfaction a year after the M&A (t = 2.24, p < .05). We replicated these results for our alternate control groups (Table D.4 in Web Appendix D).GraphTable 4. A Comparison Between M&A and Non-M&A Groups. 7 Notes: t denotes the year of the M&A activity. DID model estimation resultsWe present the estimation results of Equation 2 in Table 5. When we estimated Equation 2 with only the post-M&A variable and its interaction with M&A Group, we find that M&As caused a decline in customer satisfaction (β2 = −.635, p < .05; Model 1), which remained consistent with the inclusion of our control variables (β2 = −.754, p < .01; Model 3). We also estimated a model with only control variables (Model 2).GraphTable 5. DID Results of M&As and Customer Satisfaction with Multiple Control Groups. 8 *p < .10.9 **p < .05.10 ***p < .01.11 Notes: CSAT = customer satisfaction; ROA = return on assets; R&D = research and development. We report parameter estimates with bootstrapped standard errors in parentheses.While we accounted for time-invariant unobservable factors with a firm fixed-effects estimator, we tested whether time-varying unobservable factors altered our inference about the M&A treatment effect. We followed a procedure by [49] and used the PSACALC program in STATA. The result of this procedure suggests that our main analysis performed well because when we matched on time-varying unobservable variables, the coefficient estimate of β2 in Equation 2 only changed from −.75 to −.76. Alternate control group resultsWe analyzed Equation 2 with two alternative control groups and report their results in Table 5. When we only included firms that were similar to the focal firm in terms of predictors of customer satisfaction (control group 2), we find that M&As lowered customer satisfaction (β2 = −.622, p < .05; Model 4). When we used the propensity to engage in M&As scores (control group 3), we still find that M&As caused a decline in customer satisfaction (β2 = −.688, p < .05; Model 5). Thus, we find consistent support for a negative effect of M&As on customer satisfaction (H1a) with two alternative control groups. Conventional panel data resultsWe present our estimation results of Equation 3 in Models 1a (with a dummy variable for M&A activity) and 2a (number of M&As) in Table 6. We find that M&A activity lowers customer satisfaction (α1M&A Dummy = −2.438, p < .01; α1M&A Number = −.589, p < .01). Thus, we provide additional support for H1a and show that the negative M&A–customer satisfaction relationship is not sensitive to sampling and modeling approaches. The IMR coefficient is significant (α8 = 1.361, p < .01), which suggests that it is necessary to account for firms' propensity to engage in M&As.GraphTable 6. Effect of M&As on Customer Satisfaction Over Time. 12 *p < .10.13 **p < .05.14 ***p < .01.15 Notes: CSAT = customer satisfaction; IMR = inverse Mills ratio; ROA = return on assets; R&D = research and development. We report parameter estimates with cluster robust standard errors in parentheses. In Models 1a and 2a, the M&A variable, the IMR, and the control variables have the subscript (t), except for the market growth variable, which has the subscript (t + 1). In Models 1b and 2b, the M&A variables and their corresponding IMRs have the subscripts (t + 3), (t + 2), and (t + 1). In these models, the control variables have the subscript (t + 3), except for the market growth variable, which has the subscript (t + 4). Long-term resultsWe present the estimation results of Equation 4 in Models 1b (M&A dummy variable) and 2b (number of M&As) in Table 6. The negative impact of M&A activity on customer satisfaction persists for two years (γ1M&A Dummy = −2.032, p < .05; γ2M&A Dummy = −2.743, p < .01; γ1M&A Number = −.727, p < .10; γ2M&A Number = −1.092, p < .01). Thus, we find support for H1a even two years after an M&A. A Test of H 2 and H 3Given that we have established that M&As lower customer satisfaction with multiple methods, we aimed to test whether this decline is due to a shift in executive attention away from customers and toward financial issues (H2) and whether MROB moderates the M&A–executive attention relationship (H3). We used a four-year rolling window data structure and a SUR modeling approach to test these hypotheses. We present the descriptive statistics for this sample in Table 7, Panels A and B.GraphTable 7. Summary Statistics and Correlations. 16 *p < .10.17 Notes: CSAT = customer satisfaction; MROB = marketing representation on the board; ROA = return on assets; R&D = research and development. Identification strategy for executive attention and MROBAfter collecting data on executive attention and MROB, we had a sample of 122 firms. Arguably, executive attention to customers (vs. finance) is endogenous because executives may strategically pay attention to issues that result in better outcomes, such as customer satisfaction. To address this, we used a latent instrumental variable approach ([31]; [35]). Specifically, we used a binary unobserved instrument to separate the observed endogenous predictor into correlated versus uncorrelated components with an error term in Equation 6.2 (for further details, see Web Appendix E).It is also plausible that MROB is endogenous such that there are systematic differences between firms that appoint a marketer to their boards and those that do not. We estimated a firm's decision to have MROB ([70]) and used board-related variables (peer firm mean MROB, mean board age, chief marketing officer [CMO] on the top management team [TMT], mean board tenure, board size, chief executive officer [CEO] duality, and female percentage) as our exclusion restrictions. We estimated the following random-effects probit model to produce an MROB IMR to include in our main estimation (for the results of Equation 5, see Table E.1 in Web Appendix E): PresenceofMROBit=δ0+δ1PeerFirmMeanMROBit+δ2MeanBoardAgeit+δ3CMOonTMTit+δ4MeanBoardTenureit+δ5BoardSizeit+δ6CEODualityit+δ7FemalePercentageit+δ8Advertising/Salesit+δ9R&D/Salesit+δ10FirmSizeit+δ11IndustryGrowthit+δ12MarketShareGrowthit+δ13–34YearDummiest+υi+εit. Graph( 5)To test H2 and H3, we estimated a SUR model with moderated mediation by estimating the effect of an M&A on attention to customers (vs. finance) (Equation 6.1) and the effect of the latent instrumental variable, Attention toCustomers vs.Finance⌢ , on CSAT (Equation 6.2). We included time dummies and firm fixed effects to account for unobservable characteristics and an MROB IMR to control for selection bias. We winsorized our continuous variables and estimated the following models: AttentiontoCustomers(vs.Finance)it=β0+β1M&AGroupj+β2Post-M&At+β3(M&AGroupj×Post-M&At)+β4MROBit+β5(MROBit×M&AGroupj×Post-M&At)+β6MROBIMRit+β7(MROBit×M&AGroupj)+β8(MROBit×Post-M&At)+β9MarketSharei(t−1)+β10ROAi(t−1)+β11FirmSizei(t−1)+β12(Advertising/Sales)i(t−1)+β13(R&D/Sales)i(t−1)+β14Segmentsi(t−1)+β15MarketGrowthit+β(firmcount)FirmFixedEffects+β(t=2,3)TimeEffects(6.1)+εit, Graph(6.1) CSATit=π0+π1M&AGroupj+π2Post-M&At+π3(M&!AGroupj×Post-M&!At)+π4Attention toCustomers vs.Finance⌢it+π5MROBit+π6(MROBit×M&!AGroupj×Post-M&!At)+π7AttentiontoCustomers(vs.Finance)Residualit+π8MROBIMRit+π9(MROBit×M&!AGroupj)+π10(MROBit×Post-M&At)+π11MarketShare(t−1)+π12ROAi(t−1)+π13FirmSizei(t−1)+π14(Advertising/Sales)i(t−1)+π15(R&DSales)i(t−1)+π16Segmentsi(t−1)+π17MarketGrowthit+π(firmcount)FirmFixedEffects+π(t=2,3)TimeEffects+εit, Graph(6.2)where β3 captures the impact of M&As on executive attention to customers (vs. finance) a year after the M&A and π4 captures the impact of executive attention to customers (vs. finance) on CSAT, which allows us to test H2. β5 captures the moderating impact of MROB on the relationship between the M&A activity and executive attention to customers (vs. finance), which allows us to test H3. ResultsWe present model-free evidence of the relationship between M&A activity and executive attention to customers (vs. finance) in Figure 3. M&A firms experience a decline in executive attention to customers relative to financial issues. For example, from our sample we see that for United Airlines, executive attention to customers (vs. finance) declined 38% because of its acquisition of Continental Airlines and its customer satisfaction declined 8.2%.Graph: Figure 3. Executive attention to customer versus finance trends.When we compared a change in executive attention to customers (vs. finance) from two years before an M&A with the year before, the difference between the M&A and non-M&A groups was not significant (t = 1.54, p > .10). In contrast, when we compared a change from a year before the M&A with the year after, the M&A firms experienced a decline in executive attention to customers (vs. finance), whereas the non-M&A firms experienced a slight increase (ΔM&A Attention to Customers [vs. Finance][t + 1, t − 1] = −.03; ΔNon-M&A Attention to Customers [vs. Finance][t + 1, t − 1] = .01, p < .01). For the M&A firms with MROB, they experienced an increase in executive attention to customers (vs. finance) from the year before an M&A to the year after, whereas for those without MROB, they experienced a decrease (ΔM&A with MROB Attention to Customers [vs. Finance][t + 1, t − 1] = .01; ΔM&A without MROB Attention to Customers [vs. Finance][t + 1, t − 1] = −.04, p < .05). Consistent with this trend is the fact that for one of our sample firms, Macy's, it engaged in M&As in 2015 while having MROB. Macy's executive attention to customers (vs. financial issues) increased by 5.86% from the year before the M&As to the year after. Not surprisingly, Macy's did not experience any decline in customer satisfaction during this period.We present the estimation results for executive attention to customers (vs. finance) (Model 1a) and customer satisfaction (CSAT; Model 1b) in Table 8. We find that M&A activity is associated with lower executive attention to customers (vs. finance) (β3 = −.032, p < .05, Model 1a) and executive attention to customers (vs. finance) is associated with higher customer satisfaction (π4 = 1.423, p < .01, Model 1b). Still, the effect of M&As on customer satisfaction persists with the inclusion of the mediator, executive attention to customers (vs. finance) (π3 = −.764, p < .05, Model 1b), which suggests partial mediation. Its mediating impact persists when we incorporate the MROB interaction terms (Model 2b). Based on Model 2b, the indirect effect of M&As on customer satisfaction through executive attention to customers (vs. finance) is negative and significant (β3[Post-M&A Year and M&A group] × π4[Executive attention to customers (vs. finance)] = −.046, confidence interval = [−.144, −.000]). Thus, in support of H2, customer dissatisfaction from M&As is due, in part, to a shift in executive attention away from customers and toward financial issues.GraphTable 8. Executive Attention to Customers (vs. Finance) and MROB. 18 *p < .10.19 **p < .05.20 ***p < .01.21 Notes: CSAT = customer satisfaction; ROA = return on assets; R&D = research and development. We report parameter estimates with bootstrapped standard errors in parentheses.MROB is associated with more executive attention to customers (vs. finance) (β4 = .756, p < .01; Model 1a) and higher customer satisfaction (π5 = 48.292, p < .01; Model 1b). Further, in support of H3, MROB reduces the negative impact of M&As on executive attention to customers (vs. finance) (β5 = .623, p < .01, Model 2a) and executive attention to customers (vs. finance) is associated with an increase in customer satisfaction (π4 = 1.447, p < .01, Model 2b).[ 7] Robustness tests (Web Appendix F)We reestimated Equations 6.1 and 6.2 by adding an industry-level control variable to capture business-to-business versus business-to-customer membership, and our results do not change (Tables F.1 and F.2). We estimated firm value, firm efficiency, CSAT, and executive attention to customers (vs. finance) as a system of equations (Table F.3). Our effects of interest stay consistent when we use a four-equation model. We find robust support for our hypotheses. DiscussionWhile there has been significant research on customer satisfaction and a stream of research on M&As and financial performance, prior studies have not connected these two streams. We situate our research on this intersection and draw on the two complementary theories of the ABV of the firm and the upper echelons theory to examine the influence of M&A activity on a key, but often overlooked, stakeholder: customers. Theoretical ContributionsPrior marketing strategy research has largely overlooked how disruptive corporate transformations can be for customers. Further, it has overlooked a key pathway between M&A activity and firm value: customer satisfaction. Some empirical work (e.g., [62]) has examined the interplay between M&A activity and customer satisfaction by treating customer satisfaction as a moderator and speculated (but not formally tested) that M&As enable a dual emphasis of firm efficiency and customer satisfaction. In contrast, we show that M&As not only do not enable a dual emphasis but also cause a decline in customer satisfaction to the extent that they outweigh any gain in firm value from firm efficiency. Thus, we add to previous work on firms' dual emphasis (e.g., [43]) but show that M&A activity works against a dual emphasis of firm efficiency and customer satisfaction.We examine heterogeneity in the decline in customer satisfaction with novel conceptual additions to the M&A and customer satisfaction literature streams: executive attention to customers versus finance and MROB. We address ongoing calls to increase marketing's profile in the C-suite and higher (e.g., [24]; [46]; [70]) by examining how marketing leadership at the top of a firm redirects executive attention to customer issues, which explains differences in customer outcomes of M&As. In doing so, we add to the limited research on marketing presence in the upper echelons (e.g., [ 9]; [70]) by examining its role in channeling executive attention during M&As.Existing research in marketing has overwhelmingly used the RBV of the firm to examine outcomes of M&As. This view, which emphasizes capabilities, fails to consider executive attention ([75]); however, executive attention is a precursor to resource investments. Further, although the ABV considers executive attention, it has primarily focused on the effect of supply-side (vs. demand-side) factors that influence managerial attention (e.g., [48]; [75]). In contrast, we extend the ABV to study marketing strategy phenomena in general, and a crucial demand-side stakeholder—customers—in particular. This aligns with newer research (e.g., Vadakkepatt et al. 2021) that aligns this theory with customer outcomes.We contribute work on the marketing–finance interface. We introduce executive attention to customers versus financial issues as a mediator of the relationship between firm strategy (M&As) and a market-based asset (customer satisfaction). We find that during M&As, executives focus on financial issues at the cost of customer issues but that MROB can help mitigate this. Thus, we add a nuanced understanding the role of top leadership in navigating the marketing–finance interface.We contribute to the literature on firm-level drivers of customer satisfaction (e.g., [50]; [56]) by examining a previously ignored antecedent: M&A activity. By showing that M&As negatively impact customer satisfaction, we shed light on how higher-level strategic actions that are often motivated by shareholder motives can risk the marketing function's most prized asset, its customer relationships. Finally, we add to growing research in marketing on the use of observational inference to document the causal effects of strategic decisions. Managerial ContributionsM&As have, on average, been shown to produce adverse financial effects. This has been attributed to overpayments as a result of optimism regarding synergies and cost efficiencies. However, we suggest that firms pay a price for dissatisfying customers during the M&A process, and in fact, this effect persists two years post-M&A. This finding is critical given that a recent survey of managers suggests that expanding a firm's customer base is a primary motivation for M&As. Thus, ignoring the dysfunctional effect of M&As on customers has serious long-term financial consequences and is inconsistent with firms' M&A objectives. To demonstrate the financial impact of the M&As due to a decline in customer satisfaction, we compared the firm value of M&A and non-M&A firms due to differences in customer satisfaction and firm efficiency with the estimation results from Table 3. Compared with that of non-M&A firms, the customer satisfaction of M&A firms was 1.14% lower a year after the M&A. In contrast, compared with that of non-M&A firms, the efficiency of M&A firms was.29% higher in the same period. When we incorporated these estimates in the firm value model (Equation 1.3), we found that the value of M&A firms was 2.43% lower than the non-M&A firms a year after the M&A. To calculate a change in firm value, we multiplied the percentage difference in value between M&A and non-M&A firms by the average firm value of the firms one year after an M&A. We find M&A firms' market value was worth $481 million less than that of the non-M&A firms. Although firms may be motivated to pursue an M&A to exploit scale-related synergies that provide cost-benefits, we show that efficiency gains fail to compensate for customer dissatisfaction-related financial losses. Thus, it is critical for managers responsible for M&As and industry consultants to include a consumer impact assessment in their M&A checklists.Although there are several competing needs that require executives' attention during an M&A process, it is essential for them to allocate some of their attention to customer-related issues. The financial payoff of such attention is meaningful. To demonstrate the impact of executives of M&A firms paying attention to customers despite their tendency to focus on financial issues, we computed the percentage difference in customer satisfaction between M&A firms whose executives pay more attention to customers (vs. finance) (1 SD above the mean) and M&A firms whose executives pay less attention to customers (1 SD below the mean) with the results from Table 8 (Column 2b). Then, we used this percentage difference in customer satisfaction (.46%) to calculate a difference in firm value with the estimates from Table 3. We find that M&A firms that pay more attention to customers relative to financial issues experience 45% reduction in loss in firm value from the M&A (−1.34% vs. −2.43%). Thus, executive attention to customers can help firms significantly reduce M&As' damaging effects on customer satisfaction and firm value.Moreover, MROB can attenuate a decline in customer satisfaction and, thus, increase firm value. In our data, 27.34% of the firms had MROB at some point during the 1995–2017 sample period. To illustrate, we calculated the firm value impact of adding one marketing title to the board with the estimation results from the moderated-mediation analysis that we report in Table 8. First, we computed the percentage difference in customer satisfaction between M&A firms with no MROB and M&A firms with just one person with a marketing title on the board in the post-M&A year with the results from Column 2b of Table 8, which is 2.85%. We then used this increase in customer satisfaction from MROB to calculate firm value with the estimates from Table 3 and find that the value of a firm with just one person with a marketing title on the board in the post-M&A year was 4.28% higher compared with firms that did not have any MROB. Adding these board positions is not trivial, especially during an M&A process. However, the financial consequence of not having MROB during M&As is severe. Thus, we make the case for marketing's voice in the C-suite, which is an important MSI Tier 1 Research Priority for 2020–2022. Limitations and Directions for Future ResearchLimiting dissatisfaction from M&As is a complex task, and multiple antecedents, including deal and integration-related factors and firm-level variables that speak to other functions of the firm, should be considered. Recent research has also found that customer satisfaction has a direct positive effect on firm efficiency ([ 7]), and future research could explore this pathway in the context of M&As. Further, our sample size was limited to what the ACSI database of customer satisfaction could provide. Future studies could identify alternative data sets ([39]) to enlarge their sample to extend the time frame of the panel and data frequency to examine changes in satisfaction several years after the M&As. In addition, we study customer satisfaction with the ACSI scores of acquirer firms and not target firms. This seems reasonable given that target firms are subsumed in acquiring firms, so any post-M&A ACSI score should reflect customers of both firms. Still, future research might benefit from assessing changes in satisfaction for the target firm. The challenge is that most Compustat and ACSI data are unavailable for the target firm after it has been acquired. Alternative data sources, which include primary data on customer satisfaction at the business unit level could be a solution. Finally, we empirically show that the ABV of the firm is a viable theoretical mechanism to explain the effect of M&As on customer satisfaction and how MROB moderates this relationship. Still, a change in executive attention is one of many potential pathways from M&A activity to customer satisfaction. Future research could consider how the RBV compares with the ABV in explaining these effects. " 15,Do Backer Affiliations Help or Hurt Crowdfunding Success?," Crowdfunding has emerged as a mechanism to raise funds for entrepreneurial ideas. On crowdfunding platforms, backers (i.e., individuals who fund ideas) jointly fund the same idea, leading to affiliations, or overlaps, within the community. The authors find that while an increase in the total number of backers may positively affect funding behavior, the resulting affiliations affect funding negatively. They reason that when affiliated others fund a new idea, individuals may feel less of a need to fund, a process known as ""vicarious moral licensing."" Drawing on data collected from 2,021 ideas on a prominent crowdfunding platform, the authors show that prior affiliation among backers negatively affects an idea's funding amount and eventual funding success. Creator engagement (i.e., idea description and updates) and backer engagement (i.e., Facebook shares) moderate this negative effect. The effect of affiliation is robust across several instrumental variables, model specifications, measures of affiliation, and multiple crowdfunding outcomes. Results from three experiments, a survey, and interviews with backers support the negative effect of affiliation and show that it can be explained by vicarious moral licensing. The authors develop actionable insights for creators to mitigate the negative effects of affiliation with the language used in idea descriptions and updates.","Crowdfunding has emerged as a dominant mechanism to harness the power of crowds in raising funds for innovative ideas. Interest in crowdfunding has surged in recent years. Facebook acquired Oculus 3D visualization device, a crowdfunded idea on Kickstarter, for US$3 billion ([14]). Peloton, the highly successful exercise bike, started as a Kickstarter project. The global crowdfunding market is expected to be well over US$40 billion by 2026 ([54]). Brands such as GE ([11]) and Unilever use crowdfunding to spur innovation ([53]), and academic research on the phenomenon and its role in the digital economy is emerging ([ 2]; [12]). Crowdfunding is a form of crowdsourcing in which participants, hereinafter referred to as ""backers,"" are recruited to raise funds for ideas (e.g., [16]; [62]). As some backers fund the same ideas (i.e., ""cobacking""), overlaps develop between these backers. These overlaps, called ""affiliations,"" are key building blocks of the community's network structure and have been studied in other crowdsourcing communities (e.g., [48]). In this research, we explore how affiliation, defined as the number of cobacking relationships between potential backers and those who have previously funded the focal idea, might affect the idea's crowdfunding success. We illustrate affiliation in crowdfunding using a stylized example in Figure 1.Graph: Figure 1. Illustration of affiliation in crowdfunding.We know that crowd size affects outcomes positively as participants look to anonymous others for cues to decide which ideas to fund, a phenomenon referred to as ""herding"" (e.g., [66]). Previous research shows that attracting more backers positively impacts crowdfunding outcomes ([24]), an insight that many creators seem to grasp. However, crowd size does not represent the social structure (i.e., the pattern of connections in the community). In crowdfunding, as in other contexts in which shared communal goals exist (e.g., Wikipedia), social structure plays a more prominent role (e.g., [48]; [62]).Our primary contribution is in showing that while the total number of backers (i.e., crowd size) may positively affect funding behavior and idea success (e.g., [66]), adding backers may not be unilaterally beneficial as the ensuing affiliation between backers negatively affects funding. Our analysis reveals that the negative effect of backer affiliation is above and beyond the positive effect of number of backers (i.e., the herding effect), highlighting the tension between the benefits of adding more backers and the adverse effects of backer affiliation. In other words, while adding a new backer (e.g., the focal backer in Figure 1) may positively affect the focal idea's success, adding this focal backer may not be equally beneficial across the three scenarios in Figure 1 as the degree of affiliation differs. We propose that the affiliation between the focal backer and other backers will influence the amount that the focal backer puts toward the focal idea and, thus, the idea's funding success.Affiliation is a powerful force because it makes affiliated others' actions lead to changes in one's subsequent behavior (e.g., [35]; [57]). In some contexts, affiliation positively affects behavior as individuals desire to belong and therefore conform to affiliated others' behavior (e.g., [32]). However, in crowdfunding communities where individuals are often motivated by prosocial goals (e.g., [50]), we propose that such affiliation can negatively affect behavior. When individual actions benefit a social cause, seeing affiliated others participate may make individuals feel less of a need to do so, a process referred to as ""vicarious moral licensing"" (e.g., [13]; [22]; [37]). Thus, we propose that when backers decide whether to fund an idea, they are less likely to do so or more likely to fund a lower amount if affiliated others have already done so.While affiliations develop in the community through cobacking, creators and backers also engage through nonmonetary actions, thereby driving social interaction. Therefore, to develop further substantive implications about the effect of affiliation, we examine the moderating role of both creator and backer engagement (e.g., [ 4]; [35]). For example, creators communicate with backers through the description of the idea on its homepage, perceived to be an important determinant of an idea's success ([38]; [64]), and by posting updates about progress. Backers engage with the community by sharing ideas on social media. We aim to understand how the effect of affiliation varies due to creator and backer engagement, as they help shed light on the underlying mechanism that drives the effect of affiliation.We use multiple methods and data sets, including secondary data and experiments, to provide convergent validity to our findings. We also conduct interviews with 6 backers, survey 100 backers, and analyze 572 posts on backer forums to develop insights about the mechanism driving the effect of affiliation on funding outcomes. First, we assemble a comprehensive data set of daily funding for 2,021 new crowdfunded ideas listed on Kickstarter. We study two crowdfunding outcomes: ( 1) the monetary amount of funding received by an idea on any given day and ( 2) whether the idea raises sufficient funds during the funding window to meet or exceed its funding goal. We measure affiliation of an idea on the focal day as the number of cobacking relationships of backers who back on the focal day, with backers who funded until the day before the focal day (e.g., [35]; [41]). We estimate an instrumental variables regression model with fixed effects to assess the impact of affiliation among an idea's backers on the daily funding amount and report results from several robustness analyses. Second, we present results from controlled experiments, where we exogenously manipulate affiliation, and across three experiments, we replicate the negative effect of affiliation on funding, examine the underlying mechanism, and uncover the role of a key moderator. We find that the negative effect is stronger when creators use more communal words—both in the initial description of the idea and in subsequent updates—and when more backers share the idea on social media. Thus, creator and backer engagement may moderate prosocial motives to fund, further validating the proposed licensing mechanism.We make several contributions. We are the first to show that affiliation among backers affects crowdfunding success in statistically and economically significant ways after controlling for herding and accounting for several alternative explanations. A 10% daily increase in number of backers would lead to an additional 20.2% in funding or an increase of US$83/day (i.e., the herding effect). In contrast, a 10% daily increase in backer affiliation would lead to an 8.7% decrease in funding or a decrease of US$36/day, offsetting the increase due to number of backers by about 43%. Thus, adding backers is good, but if the additional backers increase affiliation, the positive effect of adding these backers is smaller in the scenario where affiliation is high. We isolate vicarious moral licensing as a theoretical mechanism that drives the negative effect of affiliation through experiments. We explore the role of factors related to the idea, the creator, and the backers, all of which interact with affiliation. Theoretical Background Social Influences in CrowdfundingAlthough crowdfunding has emerged as a dominant force for funding new ideas, research on crowdfunding is limited. Most early research focuses on microlending ([34]; [66]) or on crowdfunding platforms for music and journalism (e.g., [ 1]; [ 8]). Topics such as proximity to the deadline ([12]) and the text of content (e.g., [42]) have also garnered attention. Researchers have studied a variety of social factors that influence crowdfunding, in particular, the relationship between creators and individual backers, including the role of offline friendship ([34]), geographic proximity ([ 1]), and social interactions ([28]). We present a summary of representative research in Table 1.GraphTable 1. Comparison with Relevant Empirical Research. 1 Notes: IV = instrumental variable, GMM = generalized method of moments; IJC = [26].In addition to the relationship between creators and backers, there are several ways in which others' actions might inform backers' funding decisions. For example, [66] report that potential lenders assess borrowers' creditworthiness by observing other lenders. They attribute the positive effect of the number of other lenders to herding, wherein crowd size becomes a beacon for others to decide which ideas to fund. This finding might suggest that the mere addition of more supporters unilaterally benefits crowdfunding outcomes as potential backers simply follow other backers. What are some factors that might limit the positive impact of the crowd's behavior on crowdfunding? To answer this question, we note that most research has considered the presence of the anonymous crowd as the cause for a social effect that is generally positive. However, crowd size does not account for an important aspect of networks (i.e., the structure of connections among the community's participants).Thus, what is missing in extant research is an explicit acknowledgment of social structure beyond crowd size and an exploration of how it impacts crowdfunding outcomes. Social structure arises due to coparticipation in events, in our case, cobacking across ideas, a phenomenon referred to as affiliation (e.g., [17]; [60]). Affiliation, identified as an important phenomenon in the new digital economy dominated by crowdsharing ([15]), is the central focus of our research. Affiliation in CrowdfundingCommunities evolve through repeated interactions between members, which give rise to affiliations or overlaps. As affiliations grow, the interconnectivity among backers leads to scaffolding structures that hold the community together through both first- and second-order ties. Affiliations have been studied in interfirm relationships ([58]), board interlocks ([52]), product development (e.g., [35]), and wiki contributions ([48]). Regardless of the context, research suggests that ( 1) individuals notice affiliated others' behavior, ( 2) individuals feel a sense of connectedness and shared identity with affiliated others, and as such, ( 3) affiliated others' actions lead to changes in one's subsequent behavior (e.g., [35]; [57]).To establish that participants notice affiliated others' behavior when visiting crowdfunding platforms, we ran a pilot study with actual backers prescreened on the basis of their prior crowdfunding behavior. Participants were shown a screenshot of a crowdfunding page created by a web designer. To assess which information captured participants' attention, we used a standard heat-mapping approach for measuring visual attention ([ 5]). Invisible boxes around various pieces of information (e.g., idea title, backer information, idea description) coded visual attention as participants read and clicked on information, as per instructions. We found that many participants read and clicked on backer information, more so than other potentially relevant information such as the number of shares and creator information. Further, of the available backer information, affiliation ranked as highly important (for details, see Web Appendix A). Discussions on crowdfunding message boards and websites, as well as results from a survey that we conducted (discussed in the following sections), further support this idea, suggesting that among all available information, backers do consider affiliated others' behavior as they make funding decisions. Next, to confirm that affiliation affects perceptions of connectedness and shared identity in crowdfunding communities, we ran a pilot study with 150 Amazon Mechanical Turk (MTurk) participants. We find that affiliation significantly increased perceptions of connectedness and shared identity with other backers (see Web Appendix B).If potential backers notice affiliated others' behaviors and feel a sense of connectedness with these affiliated others, how might affiliated others' behavior influence their own funding decisions? To answer this important question, we next examine crowdfunding platforms and how they differ from noncommunal (i.e., transactional) contexts. Vicarious Moral Licensing in Crowdfunding CommunitiesCrowdfunding is a communal endeavor in which individuals collaborate to achieve shared goals, and platforms grow due to members' participation and interactions ([50]). Individuals behave differently in communal contexts than in noncommunal (i.e., transactional) contexts (e.g., [10]; [18]). For example, in communal contexts, individuals are more likely to request help from others, keep track of others' needs, respond to them, and report more positive emotions while doing so ([18]). In crowdfunding, these prosocial goals are reflected in the desire to help others, achieve funding goals, and be part of a community ([19]).In crowdfunding communities where individuals are often motivated by prosocial goals (e.g., [50]), we propose that seeing affiliated others fund may make individuals feel less of a need to do so, a process referred to as vicarious moral licensing (e.g., [13]; [22]; [37]). Vicarious moral licensing occurs when individuals see affiliated others' actions as satisfying their own goals, which changes their perceived moral imperative and subsequent behavior. For example, learning that affiliated others demonstrate environmentally friendly behavior makes individuals less likely to do so ([37]). It is important to recognize that this effect is not merely akin to strangers' behavior in a crowd (i.e., the bystander effect; e.g., [31]), but that it is those with whom an individual perceives a social connection (i.e., affiliation) that drives the focal effect.To confirm the importance of affiliated others' behavior and further validate the proposed mechanism, we conducted in-depth interviews of 6 backers, surveyed 100 backers, and coded 572 posts from KickstarterForum.org, the dominant crowdfunding discussion forum (see Web Appendix C). The findings confirmed the prominence of prosocial (i.e., communal) motives on crowdfunding decisions, the importance of affiliated others' behavior on backers' own funding behavior, and the role of vicarious moral licensing. For example, as one interview participant explained, ""I look at other funders only to further discover related projects. It's an interesting way to discover—because some people are more involved than you are.... It's interesting to follow that rabbit trail and see, 'Oh, this person supported this, and look at what else they fund.'"" Another stated, ""You are dealing with finite resources in terms of what you are willing to spend. If you support one thing, I don't know, for me, if I see someone supporting something else, I think, well yeah, they supported that. I'm sure I could find a bunch of other people that support a bunch of other things. I just gave X amount of dollars, whatever amount I have, and I'm not going to be giving any more than that right now.""Although we propose vicarious moral licensing as the mechanism underlying the focal effect of affiliation and initial evidence indicates this to be the case, we acknowledge the complexity of social interactions in crowdfunding. Because these social interactions are likely to be subject to several factors, we consider uniqueness as an alternative explanation for the negative effect of affiliation on funding. Backers may try to identify ideas that have received less funding from affiliated others. By doing so, backers can distinguish themselves from these affiliated others, fulfilling a need for uniqueness (e.g., [59]). In our analysis, we report results from an experiment where we test vicarious moral licensing and uniqueness as potential explanations for the negative effect of affiliation on funding. Moderators of the Effect of Affiliation on Crowdfunding SuccessCrowdfunding platforms are characterized by contributions from both creators and backers (e.g., [ 4]; [48]) as these interactions create and sustain the community's viability. Therefore, we explore the role of creator and backer engagement in moderating the impact of affiliation on crowdfunding.Previous research has found that while prosocial goals may be common in crowdfunding platforms (e.g., [50]), an idea's description can further induce prosocial motivation and behavior when it emphasizes communal language ([23]). We propose that the vicarious moral licensing effect (i.e., the negative effect of affiliation on funding) is driven by the communal context and the prosocial behavior it prompts and that this behavior is further heightened by creators describing their ideas with communal words like ""together"" and asking backers to ""partner"" with them by providing financial ""support"" ([47]). As such, ideas described with more (vs. less) communal words will exhibit a stronger negative effect of affiliation on funding outcomes.Creators can also engage with the backer community by posting updates to highlight their strategic goals and the idea's progress. Updates provide diagnostic information concerning an idea's success (e.g., [ 4]; [35]). Updates might draw backers' attention to the idea's characteristics and evolution, and lessen attention toward cobackers and affiliation. Consistent with the vicarious moral licensing mechanism, we expect updates that use more (vs. less) communal words to strengthen affiliation's negative effect. As such, we estimate the moderating effects of communal words in the creator's updates.We also explore how backers' engagement might moderate the affiliation effect by exploring the role of social media sharing of the focal idea by backers. While sharing behavior on social media could have several motivations, altruism is perceived as a primary motivator, and others seeing the shares likely view them as such ([29]; [33]). We expect that such sharing heightens funders' prosocial motives and vicarious moral licensing, further strengthening the negative effect of affiliation. Next, we describe our data and methodology. Data and MethodologyWe employed a multimethod approach to investigate the phenomenon. We collected and analyzed two types of data: observational data from a crowdfunding platform and experimental data from lab settings. We begin by describing the observational data, the empirical model and identification strategy, the results, and robustness checks. Then, we describe three experiments in which we identify the primary effect in a controlled setting and shed light on the mechanism underlying the primary effect and its moderator. The first experiment demonstrates the negative effect of affiliation on funding behavior. The second experiment validates vicarious moral licensing as an underlying mechanism and rules out uniqueness as one potential alternative explanation. The third experiment examines how the idea's description moderates the effect of affiliation. Collection and Analysis of Observational DataWe collected data on Kickstarter, the world's largest and most prominent crowdfunding platform. We utilized a web crawler to visit the new ideas page listed on Kickstarter beginning December 18, 2013. From that day and every subsequent day of data collection, the crawler visited the pages of the ideas that were started on the first day of the crawl, in addition to all the ideas that were started on the subsequent days. We stopped the crawler after 37 days, giving us data on 2,021 new ideas. We acknowledge that our research's funding constraints affected the number of days, but we went one week past the most common deadline of 30 days. We note that while some ideas in our sample received funding after data collection stopped, our results are robust to this truncation.[ 7]For the data collection, the crawler began with ideas that started receiving funds on the day of the crawl, and it identified every backer who funded the focal idea, the funded amount, and the calendar date. The crawler then visited every backer's history and collected information on all the other ideas that the backer had funded in the past. At the time of data collection, Kickstarter made all backers visible to all prospective backers. The list of backers on Kickstarter was available by clicking the ""community"" link that prominently appears on the focal idea's web page.[ 8] This process allowed us to construct the network, giving us the structure of relationships to calculate affiliation. The crawler also collected other relevant information from the page, including the idea's description, number and text of updates, and the number of Facebook shares of the idea to measure backer engagement.Our unit of analysis for the daily amount funded is an idea-day, and our final sample had 32,438 observations at the idea-day level. This specification makes the most sense because, for a data set with idea-day-backer as the unit of analysis, the funded amount (for an idea on a day) takes zero values for over 99.9% of observations, making such a specification noninformative. Next, we describe the key measures. Daily amount fundedConsistent with prior literature ([ 1]; [ 8]), our funding success measure is the amount of funding received by an idea on any given day. Across all crowdfunding platforms, this measure is always easily and prominently visible on the idea's webpage. Subsequently, we show that our results are robust to other measures of success. AffiliationConsistent with prior literature (e.g., [35]; [41]), we posit that two backers are affiliated if they have funded at least one common idea on the platform and are not affiliated if all the ideas that they have funded are mutually exclusive. Thus, the backer affiliation for a focal idea on a focal day is the number of cobacking relationships between those backers who fund the focal idea on the focal day and all backers who have funded the focal idea at any time before the focal day.Consider a backer of a focal idea who funds the focal idea on the focal day. Consider another backer of the focal idea who funds the focal idea any time before the focal day. A cobacking relationship exists between these two backers if they have both funded one idea (other than the focal idea) any time before the focal day. One cobacking relationship represents one unit of affiliation. Affiliation increases both with the number of backers who coback and with the number of cobacked ideas.To elaborate, consider the following examples. In each example, idea i is launched on day t = 1, say December 13. Further, Jack funds idea i on December 17 (t = 5), and the goal is to calculate affiliation as of December 17 (t = 5).Example 1: Tom funds idea i on December 13. Also, before December 17, Jack and Tom both fund another idea j. As there is one cobacking relationship (that between Jack and Tom for cobacking idea j), Affiliationi, t = 5 = 1.Example 2: Tom funds idea i on December 13. Jack and Jill both fund idea i on December 17. Before December 17, Jack and Tom both fund another idea j. Furthermore, before December 17, Jill and Tom both fund another idea k. As there are two cobacking relationships (those between Jack and Tom for cobacking idea j, and between Jill and Tom for cobacking idea k), Affiliationi, t = 5 = 2.Example 3: Tom funds idea i on December 13. Jane funds idea i on December 14. Before December 17, Tom, Jack, and Jane funded another idea j. As there are two cobacking relationships (those between Jack and Tom for cobacking idea j, and between Jack and Jane for cobacking idea j), Affiliationi, t = 5 = 2.We present summary statistics for Kickstarter in Table 2. The median number of backers who fund an idea in a day is 1, and most ideas have only a few backers. When a backer funds an idea, the median number of past backers of that idea is 10 backers (i.e., the median of the variable cumulative number of backers funding idea i before day t is 10). In the six months preceding data collection, 82% of backers in our data had not funded any idea on Kickstarter. Thus, the odds of having to remember multiple cobacking relationships are relatively low. Most importantly, the median value of affiliation is zero, and the mean is 3.3. In other words, a large majority of backers in our data must process a very small amount of information to infer affiliation. Our measure of affiliation reflects a more nuanced and disaggregated conceptualization of affiliations than the number of ""cobacked ideas"" or the number of ""common backers."" Other measures are likely sparser than our measure. Subsequently, we show that our results are robust to alternate measures of affiliation.GraphTable 2. Summary Statistics for Kickstarter. UpdatesWe measure the creator's engagement using the number of creator's updates on the idea page and separately measure the level of communal content in each update. To code communal words, we created a dictionary to capture words that reflect the use of communal language. For this, we asked two graduate research assistants to read descriptions of a random sample of 100 ideas (from our data) and identify words that reflected a ""communal"" idea while coding each description on whether it was communal. We provided the Merriam-Webster definition of ""communal"" (""of or relating to a community"") to the two coders along with synonyms from a thesaurus. Then, we cross-verified these words with LIWC's category for ""affiliation,"" comprising 248 words ([46]). Communal words that appear at least once in our corpus are member, team, group, groups, family, friends, affiliation, affiliate, relation, connection, alliance, relationship, partner, partners, partnership, link, merge, cooperate, cooperation, together, join, thanks, thank you, appreciate, our, and we. We measure backer engagement as the number of Facebook shares of the idea by backers, which we collected when the web crawler visited an idea's webpage. Model-free evidenceTo explore model-free evidence, we present summary statistics about three regimes of the distribution of the amount of daily funding achieved for Kickstarter in Table 3: ( 1) idea-day-specific observations when there is no funding, ( 2) when the daily funding is positive but does not exceed the mean level in the data ($409.34), and ( 3) when the daily funding exceeds the mean level. Backer affiliation is highest when ideas do not receive any funding and lowest when ideas achieve the highest funding. The measure of backer affiliation for an idea on a given day is based on cobacking relationships of backers that fund the idea on that specific day with backers who funded before that day. If no backer funds on a specific day, the affiliation measure for that day is zero. The measure is not cumulative, and it does not increase over time. Thus, there is model-free evidence for the negative effect of affiliation on funding outcomes. We collected similar data from another crowdfunding platform, Indiegogo, which we use in the robustness analysis. Additional details about the Kickstarter data and summary statistics for the Indiegogo data appear in Web Appendix D. We illustrate affiliation in Figures W1–W5 and the sample's network structure and growth in Figures W6–W8 in Web Appendix E. We estimate the primary empirical model on Kickstarter data.GraphTable 3. Means of Backer Affiliation and Other Time-Varying Covariates at Different Levels of Daily Funding (Kickstarter). Empirical modelFollowing [ 8] and [66], our primary dependent variable (yit) is the monetary funding received by an idea i (i = 1,...N) on day t (t = 1,...Ti). As a starting point, we incorporate backer affiliation and several controls in a fixed-effects regression model as follows: log(yit)=αi+αt+β1log(Affilit–1)+β2log(CumBackersit–1)+β3log(CumUpdatesit–1)+β4log(yit–1)+β5PropGoalit–1+β6PropDurationit–1+β7LastWeekit–1+β8Networkit–1+β9log(CommunalUpdatesit–1)+β10log(Affilit–1)×CommunalUpdatesit–1+β11log(Affilit–1)×FBSharesi+eit. Graph( 1)To account for nonnegativity, we log-transform all variables that are not proportions. For variables that can take zero values, we take the logarithm of the variable added to.001. Replacing this constant with other constants does not affect our results. Estimating the model without taking logarithms of any variable gave us consistent results.To control idea-specific confounding factors such as inherent differences in idea quality, the novelty of idea description, creator expertise, and so on, we employ idea-specific fixed effects αi, a vector of 2,021 elements for the Kickstarter data set. We incorporate fixed effects for each day in the idea's funding window to control temporal patterns in funding and changes in the Kickstarter environment over time. These are denoted by the vector αt. Error terms are assumed normally distributed and clustered at the idea level.Our key independent variable is Affilit − 1. This is the number of cobacking relationships between those backers who fund idea i on day t − 1 and all backers who have funded this idea before day t − 1. Subsequently, we report robustness checks to alternate measures of backer affiliation. Although our fixed-effects specification controls for confounds at the idea level and the day level, we need to control idea-specific factors that are time varying. Chief among these is the amount of funding received by the focal idea on day t − 1 ([ 8]), enabling us to control those time-varying idea-specific unobservables, which may be serially correlated (e.g., word of mouth about the idea) and to attenuate serial correlation among the residuals. This also accounts for the alternate explanation that affiliation on day t − 1 affects funding on day t − 1, but not on day t. By incorporating the lagged measure of funding, we can account for all factors that affect funding until the day t − 1.We next discuss other time-varying idea-specific controls. First, the number of affiliations among backers is correlated with the number of backers. There can be no backer affiliations without backers; more backers could result in more possibilities for affiliation. To control for the possibility that the number of backers drives the effect of affiliation on funding, we include CumBackersit − 1, the cumulative number of backers funding idea i by day t − 1, as a control variable. Also, to the extent that ideas with more backers attract more funding ([66]), this serves as a measure for herding behavior. Second, creators communicate with backers via updates, a means to elevate idea visibility and signal effort ([12]). To understand how creator actions might drive funding, we include CumUpdatesit − 1, the cumulative number of updates by the creator of the idea i by day t − 1. In addition, CommunalUpdatesit − 1 is the number of communal words contained in the updates.Third, the funding window of an idea influences its funding outcomes. Ideas receive more funding in the later stages of the funding window as the funding deadline nears (e.g., [12]; [31]). To account for this, we include the duration of the funding window completed for the idea (PropDurationit − 1) as a proportion of the total funding window (typically 30 days). Furthermore, ideas receive greater funding as they get closer to meeting their funding goals ([12]). Although daily fixed effects account for temporal variations in funding, they might not capture the effect of proximity to the funding goal. Therefore, we include PropGoalit − 1, the proportion of the funding goal of the idea that has been achieved until day t − 1, and LastWeekit − 1, a dummy variable for whether the observation belongs to the last week of the funding window.Finally, structural measures of network centrality might affect the outcome. Because these measures capture the extent of social capital that accrues to ideas due to being associated with certain backers, we want to control for the effects of these measures. We compute and include three of the most widely used network measures in marketing (e.g., [35]; [48]; [58]), (Networkit − 1): closeness centrality, betweenness centrality, and eigenvector centrality of idea i on day t. Closeness centrality in our context is how close the focal idea is from all the backers (connected and not connected) in the network, betweenness centrality is the extent to which the focal idea lies on the common paths between all pairs of backers in the network, and eigenvector centrality is the extent to which the focal idea's backers are prolific in backing other ideas. We computed both bipartite and single-mode network variants of each of these measures.[ 9] Given the high correlation across the bipartite and single-mode versions of each measure, we included in the model the version of each measure that leads to a more significant improvement in R2. As shown in the correlation matrix of all variables (Table 4), these variables are not highly correlated with our measure of affiliation, suggesting that affiliation captures the network's unique structural properties based on counts of overlaps. To assess interaction effects, we interact affiliation with CommunalUpdatesit − 1[10] and with the number of Facebook shares of the idea by backers (FBSharesi).GraphTable 4. Pairwise Correlation Coefficients of All Variables (Kickstarter). 2 Notes: We take logarithms of all variables, which are not proportions. For variables that can take zero values, we take the logarithm of the variable added to.001. All variables pertain to the focal idea. Coefficients with p < .05 are in boldface.We use lags of all covariates because information about the focal day is not updated in real time and is unavailable until the following day.[11] We present the correlation matrix of all variables in Table 4; most correlations are less than.3, allaying multicollinearity's ill effects. Empirical strategyWe first discuss how our work is different from the peer effects literature and then explain our identification strategy. The prototypical problem in the marketing literature on the identification of peer effects (e.g., [40]) is to estimate the likelihood of agent A adopting a product (e.g., buying an online game) under the knowledge that agent B (a self-identified ""friend"" or influencer) has already adopted that same product. The herding literature has conclusively documented positive peer effects across various consumer contexts (e.g., [57]; [66]). If there are positive effects due to the size of the crowd or the number of peers, that would be equivalent to herding, not our study's primary focus. In other words, our main objective is not to estimate how backer A will fund the focal idea if another backer B has previously funded it. We account for herding in our model by incorporating the prior number of backers of the focal idea as a control. Instead, our objective is to study the effect of affiliation, which is formed when two backers back an idea that is not the focal idea. Affiliation arises in collaborative contexts (e.g., board interlocks, product development teams) rather than common product purchases. We note that this is a key difference of our article from other contexts. In addition, our interest is less in modeling agent behavior (e.g., an individual's rating of a product in [57]) than in modeling product success (i.e., funding success of an idea). We next address three main issues that could confound identifying the causal effect of affiliation on the focal idea's funding. Correlated unobservablesIdea-specific characteristics that are not observable to the researcher could be correlated with our affiliation measure and affect the focal idea's funding. Perhaps highly affiliated backers are attracted to ideas with high (or low) unobserved quality. The inability to control for quality dimensions might induce an upward (or downward) bias in our estimate of the effect of affiliation. Following [40] and [57], we incorporate idea-specific fixed effects. These effectively control for all idea-specific factors that might be correlated with affiliation. Next, there could be time-varying factors across the funding window that might be correlated with affiliation and funding. For example, affiliation and funding are both likely to be low in the first few days of funding. We control for all day-specific trends by incorporating day fixed effects. Finally, the presence of idea-specific time-varying factors cannot be ruled out. We control for funding received by the focal idea on day t − 1. As mentioned previously, this approach enables us to control those time-varying idea-specific unobservables, which may be serially correlated, and to attenuate serial correlation among the residuals. SimultaneityIn the context of peer influence, simultaneity implies that not only can the influencer influence the focal individual but the focal individual could also affect the influencer's actions, leading to an upward bias in the estimate of peer effects. In our context, affiliations formed on a focal day may affect the focal idea's funding. Simultaneously, the focal idea's funding on a focal day also affects affiliation formation on that day. Following recent literature (e.g., [45]), we use the lagged measures of affiliation in the model. While affiliation before the focal day can affect funding on the focal day, the reverse is not possible. Endogenous group formation (or homophily)Backers with similar preferences may be more likely to behave similarly. In such a scenario, the effect of prior affiliation on subsequent funding of the focal idea might manifest these common preferences. The literature on consumer peer effects has used consumer-specific fixed effects to deal with this. However, crowdfunding is different from consumer contexts in that while consumers buy (and evaluate) several products, backers typically fund very few ideas on a platform.Moreover, unlike crowdfunding, consumer contexts generally focus on the individual more than collective action ([50]). So, backer-specific fixed effects are econometrically infeasible to estimate for both the researcher and the platform. Instead, we first include the cumulative number of backers and several other network measures as controls. Next, we note that controlling for lagged funding of the idea also controls backer characteristics that have affected funding before the focal day.Finally, we include an instrument for affiliation. If our measure of affiliation is correlated with the error term in Equation 1, its coefficient could be biased. In our primary analysis, we use an observed instrument to estimate a two-stage least-squares instrumental variable regression model. As [49], p. 4) mentions, the ideal solution for endogeneity is to conduct an experiment where the endogenous variable is uncorrelated with the construction's dependent variable. Therefore, we ran controlled experiments, which we explain subsequently, where participants were randomly assigned to different affiliation levels, creating exogenous variation.For the primary instrumental variable approach, we follow recent research (e.g., [20]; [51]) that uses instruments based on agent behavior in categories (or firms) different from the focal category (or a firm). Following this approach, we use the mean (across ideas) of affiliations on day t − 1 of all ideas in our Kickstarter data, which are in a category different from that of the focal idea as the primary instrument for Affilit − 1 in Kickstarter. For example, for an observation about an idea on movies on December 22, this instrument is the mean of affiliations on December 22 of all ideas in our data that are not in the movies category. This instrument is correlated with Affilit − 1 (correlation = .16).Conceptually, this instrument is appealing because of the interdependencies across different parts of the global affiliation network on Kickstarter (i.e., the affiliation network across all ideas seeking funding concurrently), thus satisfying the relevance criterion. However, because most backers only back one idea (i.e., affiliation is sparse), the mean affiliation across ideas in other categories is very unlikely to be related to the unobserved component of the focal idea's funding outcome in Equation 1 providing the basis for identification. Further, a category-level measure of affiliation should remain unaffected by idea-level factors, especially if the idea is from a different category. A category-level measure should not correlate strongly with idea-day-level idiosyncratic shocks from another category, thus meeting the exclusion criterion. The first-stage equation is specified as log(Affilit–1)=λ0+λ1IVit–1+λ2log(CumBackersit–1)+λ3log(CumUpdatesit–1)+λ4log(yit–1)+λ5PropGoalit–1+λ6PropDurationit–1+λ7LastWeekit–1+λ8Networkit–1+λ9log(CommunalUpdatesit–1)+δit–1. Graph( 2)The R2 for the first stage regression without the instrument (i.e., assuming that λ1 = 0) is.365 and with the instrument is.385, showing that the instrument's addition improves the in-sample model fit. The estimate of λ1 is.42 (p < .01). The corresponding F-statistic for the F-test of excluded instruments is 879.83, far exceeding the threshold value of 10 ([55], p. 522). The large value of the Anderson–Rubin statistic (F( 1, 28,300) = 298.41) rejects the null hypothesis that the instrument is weak. We show in robustness analyses that the estimates are consistent across the use of alternative instruments. We also instrument for the interaction of affiliation and the number of communal words contained in the updates (CommunalUpdatesit − 1). Following [44], the instrument for this interaction variable is the interaction of the instrument for affiliation and CommunalUpdatesit − 1. We do not instrument for the interaction of affiliation and the number of Facebook shares, because the Durbin–Wu–Hausman test of the hypothesis that this regressor is exogenous could not be rejected (χ2 = .055, p > .1). Furthermore, the sharing activity of a specific idea on a social media platform other than Kickstarter is conceptually independent of its funding outcome on Kickstarter. ResultsFirst, we present the parameter estimates of the instrumental variable regression models estimated on the Kickstarter data and then discuss robustness checks. We present estimates of five models, with and without instruments, and the sequential addition of interactions in Table 5. M1–M4 do not have interaction effects, and while M1 ignores endogeneity, M2, M3, and M4 correct for it and show that the results are robust to different instruments. The results from the full model specified in Equation 1 are reported in M5, which we discuss next.GraphTable 5. Coefficient Estimates of the Fixed-Effects Regression Model of Daily Funding of Ideas on Kickstarter. 3 *p < .10.4 **p < .05.5 ***p < .01.6 Notes:""Constra"" refers to [ 7] measure of constraint of the focal idea. ""Other"" instrument refers to the instrument constructed from Indiegogo data. Full model resultsWe find that affiliation among backers has a consistent negative effect on the funding of ideas on Kickstarter (β = −.87, p < .01). This effect persists despite the inclusion of idea-specific fixed effects, daily fixed effects, controlling for lagged funding, and the prior number of backers of the idea. We corroborate extant findings on herding (e.g., [66]) and additionally show that affiliation plays a key role and that its effect is negative.Concerning the moderators, the creator's engagement measured as using communal words in updates further strengthens the negative effect of affiliation, perhaps because of a heightened licensing effect (β = −7.68, p < .01). For backer engagement, we find the negative effect of affiliation is stronger as backer engagement, measured as the number of Facebook shares of the idea by backers, increases (β = −.006, p < .01). One explanation of this is that while individuals share on Facebook for various motives, the primary motivation is prosocial, and others seeing the shares likely see them as such, strengthening the vicarious moral licensing effect ([29]).Concerning control variables, the greater the number of backers of an idea before the focal day, a measure of herding, the more funding the idea will attract on the focal day (β = 2.02, p < .01). This indicates that the total number of backers for an idea may act as a signal of its quality or potential worthiness, a finding that is consistent with prior research (e.g., [34]). The current research replicates this effect and demonstrates that social structure influences behavior beyond the herding effect. Moreover, this theory supports our contention that affiliation, measured by cobacking, drives the negative effect, not herding. We also find that the total number of updates posted by the creator has a negative effect on crowdfunding success (β = −.05, p < .10), although this effect is not significant across all model specifications. The effect of the proportion of the funding goal which was achieved on the previous day is negative (β = −.17, p < .05), perhaps suggesting a preference to fund underfunded ideas. For network centrality measures, we find that betweenness (β = −.02, p < .05) and eigenvector centrality (β = −4.40, p < .05) have a negative effect on funding. The negative effects of these second-order network measures, compared with the positive effect of number of backers (proxy for first-order network effect), highlight the complexity in flow of information on the network and are consistent with findings from prior studies (e.g., [35]). This is perhaps because these measures indicate the backers' ability to identify and fund salient opportunities, or access to information from their overall networks about idea quality based on indirect ties across the whole network, not just direct ties. Thus, the effects also highlight the importance of distinguishing direct and indirect aspects of how networks operate in community contexts.To ensure that outliers are not driving our results, we estimate the main model (M5) after dropping the top 10th percentile of observations (which have affiliation values greater than 7), yielding a significant and negative estimate of the affiliation coefficient (β = −.87, p < .01). We find a similar negative effect in models estimated on various subsets of the data. To investigate if specific categories of ideas drive our results, we estimate the model separately for each category's ideas. We find a negative effect of affiliation for 11 out of 12 categories, with the most negative effect of affiliation in the ideas from the photography and technology categories. Our estimate of affiliation's effect is negative but not statistically significant for the ""dance"" category, which accounts for just 21 out of 2,021 ideas in our data. Robustness analysesWe conducted several robustness analyses. First, we estimated the model on Indiegogo data; the results are quite consistent (see Web Appendix F). Second, we estimated the model on Kickstarter data using three alternative sets of instruments: discrete latent instrumental variables, an instrument constructed using affiliation from another platform, and a network-based instrument (see Web Appendix G). Third, we estimated probit, logit, and Tobit models of funding success and checked the robustness of our results to two alternative measures of affiliation (Web Appendix H). All analyses show that our results are robust. Next, we report three experiments in which we probe the effect of affiliation, the underlying process, and a moderating factor to further validate our empirical model. Collection and Analysis of Experimental DataIn the first experiment, we demonstrate the negative effect of affiliation in a controlled experimental setting. In the second experiment, we validate vicarious moral licensing as an underlying mechanism and rule out uniqueness as one potential alternative explanation. In the third experiment, we show how the idea's description might moderate the effect of affiliation. Experiment 1We conducted Experiment 1 on MTurk with 200 North American residents[12] (Mage = 35.26 years; 49.8% women; 42.6% have previously funded a crowdfunding idea). We presented participants with two ideas seeking funding (both real ideas from Kickstarter; see Web Appendix I). First, participants saw a screenshot of a website created by a graphic designer to look like an idea page on a real crowdfunding platform (e.g., [ 8]; [65]).Consistent with prior research, participants were given money beyond study payment, creating an incentive-compatible dependent measure ([21]; [39]). Participants were told, ""As part of this study, you will receive a $2 bonus. You can use some or all of this money to fund this project."" They were then asked how much they would give toward the idea on a nine-point scale with dollar amounts in $.25 intervals, ranging from $0 to $2.00. If participants chose ""$0"" and opted to keep the full bonus, they were then forwarded to the end of the survey and were paid the original MTurk fee as well as the $2 bonus. If participants used any of their bonus to fund the first idea, they were included in our primary analyses. Ninety-three participants opted not to fund the first idea, leaving us with 107 participants. Four participants were removed who indicated that they had a child affected by autism, the focus of one of the two ideas, and were inclined toward funding but would opt to put the money toward helping their child. All participants completed the dependent measures. Two participants were removed for spending less than a second on the manipulation, leaving us with 101 participants. Participants then saw a screenshot of a second website designed to look like an idea on a crowdfunding platform (for details, see Web Appendix I).The screenshot included idea information and a list of recent backers shown on the screen's right side. Participants in the high-affiliation condition saw a high overlap in the number of backers across the two ideas. Participants in the control-affiliation condition saw the same number of backers, but the names on the two lists did not overlap. A manipulation check confirmed the effectiveness of the manipulation. All participants who funded the first idea were told that they would receive an additional $2 bonus to keep or use to fund the second idea. Their decision on a nine-point scale ranging from $0 to $2.00 in $.25 intervals served as the outcome. At the end of the study, participants were given the money that they chose to keep as a bonus, and the remainder (i.e., what they chose to fund each of the ideas) was put toward each crowdfunding idea. Finally, participants responded to a set of demographic measures (e.g., age, gender, whether they had previously funded an idea on a crowdfunding platform). A one-way analysis of variance showed a significant effect of affiliation on the funding of the second idea (F( 1, 99) = 4.05, p < .05). As we expected, those in the high-affiliation condition funded less than those in the control-affiliation condition (Mhigh = 4.27, SD = 2.27 vs. Mcontrol = 5.27, SD = 2.70). Of the $2 bonus, those in the high-affiliation condition chose to fund $.82 toward the focal idea, while those in the control-affiliation condition chose to fund $1.07, on average.The first experiment confirmed the negative effect of affiliation in the lab setting, validating our primary empirical finding that affiliation negatively affects crowdfunding success. Experiment 2In the second experiment, we measured two potential mediators in an attempt to document ""a"" mediating process (i.e., the mediating process given our stimuli and procedures) as opposed to ""the"" mediating process (i.e., a single mediating process that is operative across all crowdfunding contexts; e.g., [ 6]). We propose vicarious moral licensing as a mechanism for the negative impact of affiliation on funding and test need for uniqueness as an alternative mechanism ([59]).We conducted the study on MTurk with 228 North American residents (Mage = 39.57 years; 54.4% women; 38.2% had previously funded an idea on an online crowdfunding platform). All participants spent adequate time on the manipulation. Three participants did not complete the dependent measures, resulting in an effective sample of 225 participants. Participants were told to imagine that they had $50 and were asked to choose one idea to fund from a set of four real ideas seeking funding on Kickstarter and across categories (e.g., technology, nonprofits, arts/film); details appear in Web Appendix I. After this decision, they read about a second idea that they were told is seeking funding. Those in the high-affiliation condition were told that many of the backers who funded the first idea they chose also funded the focal idea. Those in the control affiliation condition were provided no information about other backers' funding decisions. A pretest confirmed the effectiveness of the manipulation (see Web Appendix I). Next, participants responded to two items to capture vicarious moral licensing (""Based on the funding behavior of cobackers, I do not feel the need to fund [focal idea]"" and ""Based on the funding behavior of cobackers, I do not feel obligated to fund [focal idea]""; M = 4.10, SD = 1.48; r = .72) and two items to capture uniqueness (""If I funded [focal idea], my decision to fund would say a lot about me as a unique individual"" and ""If I funded [focal idea], it would help me stand out from the crowd""; M = 3.68, SD = 1.45; r = .81).Next, we asked participants how much money they would pledge toward funding the subsequent focal idea (range: $0–$5,000, the total needed to hit the focal idea's funding goal). Consistent with prior research and our empirical model, we log-transformed funding ([36]). Finally, participants completed demographic questions.As expected, we found a negative effect of affiliation on funding (F( 1, 223) = 4.29, p < .04) such that those in the high-affiliation condition reported a lower funding amount than those in the control condition (Mhigh = 3.17, SD = 2.49 vs. Mcontrol = 3.82, SD = 2.24) or in raw numbers (Mhigh = $256.96, SD = $674.40 vs. Mcontrol = $339.88, SD = $875.90). A one-way analysis of variance showed a significant effect of affiliation on the licensing measure (F( 1, 223) = 3.89, p = .05). As we expected, those in the high-affiliation condition agreed more with the licensing measure, indicating less need to fund than those in the control condition (Mhigh = 4.29, SD = 1.57 vs. Mcontrol = 3.90, SD = 1.37). However, there was no significant effect of affiliation on uniqueness (Mhigh = 3.56, SD = 1.52 vs. Mcontrol = 3.80, SD = 1.36; F( 1, 223) = 1.53, p = .22). We then assessed the indirect effects of the two mediators on funding. The results indicate that licensing was a significant mediator (95% confidence interval does not include 0: [−.4423, −.0003]), but uniqueness was not (95% confidence interval: [−.5479,.1142]).In this experiment, we replicated the negative effect of affiliation and uncovered vicarious moral licensing as an underlying mechanism. Although we did not find an effect of affiliation on uniqueness in this study, we note that uniqueness may operate more strongly for some ideas and some individuals, providing an interesting avenue for future research on crowdfunding ([59]). Experiment 3In Experiment 3, we explored the role of a moderator: how the creator describes the idea. We theorized that the negative effect of affiliation occurs in a crowdfunding context, at least partly due to its communal nature and how the ideas are presented to potential backers. We conducted the third experiment on MTurk with 206 North American residents (Mage = 38.81 years; 46.1% women; 42.2% have previously funded an idea on an online crowdfunding platform). All participants completed the dependent measures. Three participants who spent less than one second reading the manipulation were removed, resulting in N = 203. We manipulated two factors between participants: ( 1) affiliation (high vs. control) and ( 2) idea description (more vs. less communal).As in Experiment 2, participants read about an idea currently seeking funding on Kickstarter and were told to imagine that they had funded this idea (see Web Appendix I). We used the same manipulation of affiliation as in Experiment 2. Those in the high-affiliation condition were told that many backers who funded the first idea they chose also funded the focal idea. Those in the control-affiliation condition were not provided any information about other backers' funding decisions. Participants then read about diveLIVE, a technology that allows divers to talk underwater while streaming live video to the internet. diveLIVE, the focal idea, was described as more or less communal with small changes (e.g., ""Let's learn about the oceans"" vs. ""This product uses technology to take videos of the oceans"").Next, participants indicated how much money they would pledge toward diveLIVE, the focal idea (range: $0–$20,000, the total needed to hit the focal idea's funding goal). Consistent with prior research, our empirical model, and Experiment 2, we log-transformed funding ([36]) for analysis but provide results in raw numbers for ease of interpretation. Finally, participants completed demographic questions.We found evidence for a main effect of idea description (F( 1, 199) = 13.86, p < .01) consistent with prior research, which finds that ideas described as more communal tend to be more successful than those described as an investment opportunity ([ 3]). More importantly, we found an interaction between the two manipulated factors (F( 1, 199) = 5.84, p < .02). As we expected, when the idea was described as more communal, those in the high-affiliation condition reported lower funding than those in the control-affiliation condition (Mhigh = $2,155.32, SD = $3,998.08 vs. Mcontrol = $4,073.04, SD = $5,316.08; t(199) = 2.09, p < .04). When the idea was described as less communal, there was no effect of affiliation on funding (Mhigh = $2,572.33, SD = $4,933.01 vs. Mcontrol = $1,868.68, SD = $4,039.03; t(199) = −1.34, p = .18; see Figure W9 in Web Appendix I). The third experiment established that the negative effect of affiliation is stronger when creator's use more communal words in the description of the idea. Validating Moderation with Observational DataAs discussed previously, we find a negative moderating effect of the number of communal words in updates posted by creators. To validate the third experiment with converging evidence, we returned to our secondary data to examine how the number of communal words in the idea description influenced the relationship between affiliation and funding behavior across thousands of crowdfunding ideas (e.g., [42]). This would establish how the use of communal words in creator's updates as well as in the idea's description would influence the effect of backer affiliation and highlight the importance of the communal mechanism. We used the same text dictionary that we created for coding communal words in updates and coded the description of every idea in our sample. The median number of communal words in an idea description is 3 (M = 6.1). We then created two subsets of our data based on a median split of the number of communal words used in describing the idea. We estimated the model separately on each subset and find that the coefficient of affiliation is less negative for ideas described using three or fewer communal words (M = −.92, SE = .09) than for ideas described using four or more communal words (M = −1.25, SE = .15). Replacing the number of communal words in this analysis with the ratio of the number of communal words to the total number of words does not affect this result, nor does splitting the data on the basis of the average number of communal words instead of the median. Finally, the effect of affiliation is less negative for ideas with no communal words than for ideas with at least one communal word. This provides real-world evidence for the role of idea description on the relationship between affiliation and funding behavior, validating our theory and experimental evidence.In summary, these findings further support our reasoning that the negative effect of affiliation is driven, at least in part, by the communal nature of crowdfunding and the prosocial mindset that it prompts ([50]). When an idea is described as more communal, these prosocial goals are exacerbated, leading potential backers to feel that they do not need to fund the idea because these affiliated others are funding it (e.g., [37]). However, when an idea is described as less communal, this effect is mitigated. Next, we discuss our results and develop implications for theory and practice. DiscussionWe establish a negative effect of affiliation on the crowdfunding success of ideas using a large empirical study and then validating the effect through experiments. We provide preliminary insights into the role of vicarious moral licensing as the underlying mechanism for this effect and investigate the moderating role of creator and backer engagement. The licensing effect and its role in reducing backers' perceived obligation to fund ideas could make backers less likely to fund or fund with less money if they opt to fund, both of which could explain the negative effect at the idea level. We begin with a focus on the novel contribution of our finding concerning affiliation, discuss the economic implications of our results, and identify the primary contributions of our research and how it paves the way for future research.The negative effect of affiliation among backers in crowdfunding is distinct from and in addition to the positive effect of herding due to the crowd's size shown in prior research (e.g., [66]). We establish an inherent tension between the positive effect of crowd size and the negative effect of backer affiliation in crowdfunding. Thus, we show that, in addition to relying on crowd size, backers make inferences based on the behavior of affiliated others in a crowdfunding context. A 10% daily increase in number of backers leads to an additional 20.2% in funding or an increase of US$83/day (i.e., the herding effect). In contrast, a 10% daily increase in backer affiliation leads to an 8.7% decrease in funding or a decrease of US$36/day, offsetting the increase due to number of backers by 43%. Our results concerning affiliation are both statistically and economically meaningful and highlight the need to recognize the tension between increasing the number of backers and limiting the ill effects of affiliation.Interestingly, Kickstarter stopped disclosing the prior backers' list on an idea's page as of the time of writing this article. This policy change is consistent with our results. If backer identities remain unknown, potential backers cannot infer affiliation, and therefore ideas cannot be negatively impacted by backer affiliation. Other crowdfunding platforms should reevaluate disclosure policies about past backers of an idea or perhaps reconsider whom they show at the top of their backer lists.So how might creators mitigate the negative effects of affiliation? The moderation effects from our results provide actionable insights for creators seeking crowdfunding from potential backers and considering what platforms to pursue. Our results concerning the interaction between affiliation and creator engagement show that creators can subdue the negative effects of affiliation by carefully crafting the idea description and updates, avoiding communal language.Further, while it appears that encouraging backers to share the idea on social media might be counterproductive because it strengthens affiliation's negative effect, the impact is small and should not be a major concern. The change in the marginal effect of affiliation as sharing by backers increases is small, indicating that change in backers' engagement, while statistically significant, does not have a meaningful effect on crowdfunding. Doubling the number of Facebook shares from its mean of 79 to 148 strengthens the negative effect of affiliation by.42% and translates to a decline of US$1.72/day.We developed recommendations for creators and examples of best practices from our data set (see Table 6). For example, creators should focus on the idea's inherent purpose and objective value in its description and avoid using too much communal language (e.g., cooperate, partner, support) in the idea description and updates. Overall, we recommend that platforms educate creators on how best to structure communication with backers and guide creators in meeting their goals. Backers could perhaps learn to interpret such updates better and use the information provided by the backer to qualify what they infer from the community.GraphTable 6. Actionable Outcomes for Managers Recommendations for Idea Descriptions and Updates. Our results about the mechanism provide insights on how platforms and creators should engage with backers. Research has shown that licensing is a nonconscious effect and can be mitigated by making individuals aware of their behavior ([27]). Particularly in this type of vicarious moral licensing, highlighting individuals' uniqueness and independent identity may also mitigate the negative effect of affiliation on funding ([30]; [37]; [43]). If creators expect high overlap among backers, they could describe their ideas using less communal language, thereby lowering the licensing effect. Our results suggest that vicarious licensing might overwhelm other relevant idea information, potentially leading to suboptimal backer decisions. In line with our findings, backers might, in some cases, pay more attention to signals from affiliated others rather than from the whole crowd.For crowdfunding platforms, our findings provide a rationale for why there might be room for new crowdfunding platforms to thrive and grow. Although several crowdfunding platforms have flourished in the past decade, Kickstarter, Indiegogo, and GoFundMe have arguably dominated the market. Other once-popular platforms, such as Sellaband and PledgeMusic, have failed. Large platforms with millions of backers might pose high entry barriers to new entrants. However, our findings point to one source of competitive advantage for newer platforms: negative affiliation effects are more likely to occur in well-established platforms with large backer communities. Strategically building diverse and unaffiliated communities of backers might confer a competitive advantage to new platforms. Our results show that this can be achieved by expanding the number of categories of ideas, as affiliation's negative effect may be mitigated as backers of ideas across different categories may be less likely to coback ideas. The failure of category-specific platforms such as Sellaband (music), and the relative success of platforms hosting diverse ideas, such as Kickstarter, provides support for this reasoning. Second, platforms allocating marketing resources across existing and new backers (e.g., allocating social media spending across established markets such as Los Angeles and new markets such as Lima) could perhaps view our results as a reason to divert resources away from backer-dense markets. Third, platforms that provide backer information may also want to use algorithms that promote unaffiliated (vs. affiliated) backers, for example, by highlighting first-time backers. Finally, drawing on our results about creator engagement, we recommend that platforms educate creators on how to design better backer communication.Insights from our study are relevant to other types of crowdsourcing platforms as well. For example, participants on LEGO's Ideas, which focuses on ideation, and SeedInvest, which helps raise equity, could mitigate the negative effects of affiliation, for example, by describing initiatives as less communal and by posting updates with less communal language. Our findings are also applicable to crowdfunding contests (e.g., [ 9]; [25]), where participants could be encouraged to vote across categories to reduce coparticipation and help them break away from the adverse effects of groupthink.We highlight several areas of inquiry for future research. Reward structures could impact the role of affiliation in crowdfunding and thus merit attention (e.g., [56]). Fake reviews have been investigated in the online context (e.g., [67]), and it would be interesting to explore the veracity of idea descriptions and creator updates. In addition to affiliation, which we study, other network characteristics such as clans and core–periphery structures ([60]) could explain the nature of information flow across affiliation structures.As interest in crowdfunding increases, interesting research questions continue to emerge. We believe that our research explores important questions concerning crowdfunding that involve backer affiliation and community structure, and we hope to lay the foundation for future studies in the domain. " 16,Do Marketers Matter for Entrepreneurs? Evidence from a Field Experiment in Uganda," Promoting growth by differentiating products is a core tenet of marketing. However, establishing and quantifying marketing's causal impact on firm growth, while critical, can be difficult. This article examines the effects of a business support intervention in which international professionals from different functional backgrounds (e.g., marketing, consulting) volunteered time to help Ugandan entrepreneurs improve growth. Findings from a multiyear field experiment show that entrepreneurs who were randomly matched with volunteer marketers significantly increased firm growth: on average, monthly sales grew by 51.7%, monthly profits improved by 35.8%, total assets increased by 31.0%, and number of paid employees rose by 23.8%. A linguistic analysis of interactions between volunteers and entrepreneurs indicates that the marketers spent more time on product-related topics than other volunteers. Further mechanism analyses indicate that the marketers helped the entrepreneurs focus on premium products to differentiate in the marketplace. In line with the study's process evidence, firms with greater market knowledge or resource availability benefited significantly more than their peers when matched with volunteer marketers. As small-scale businesses form the commercial backbone of most emerging markets, their performance and development are critically important. Marketers' positive impact on these businesses highlights the need for the field's increased presence in emerging markets.","Most of the businesses are too small and utterly undifferentiated from the many others. —[ 6], p. 218) on entrepreneurial businesses in emerging marketsWhat role, if any, do marketing professionals play in improving the world? We propose that marketers help firms grow profitably, and their positive effects can be tremendous, especially when considering entrepreneurial firms in emerging markets. Flourishing entrepreneurs create jobs and wealth and help improve overall living standards ([ 2]; [ 6]; [14]; [47]). In the words of [20], p. 196), ""Entrepreneurship is one of the most effective means to alleviate poverty in developing countries.""Entrepreneurs are ubiquitous in emerging markets ([22]). In 2010, more than 31% of the adult population in Uganda, the setting for our study, was either starting a business or running a business less than four years old ([29]). However, many emerging-market entrepreneurs struggle to make ends meet, and their firms' growth rates are low ([26]; [31]), stifling the positive impact they could have on society ([20]). As [ 6] assert, the low growth rates seem to result from most businesses being ""utterly undifferentiated"" and failing to attract customer interest.Marketing helps firms differentiate by attempting to answer the question, ""Why should the customer buy from the firm and not elsewhere?"" (see, e.g., [10]; [33], p. 5). Thus, we examine whether entrepreneurs in emerging markets can benefit from marketers' help. As Figure 1 shows, we conducted a randomized controlled field experiment with 930 entrepreneurs to examine a virtual business support intervention in which international professionals from different functional backgrounds volunteered their time supporting Ugandan entrepreneurs via Skype video conferencing, mobile calls, emails, WhatsApp, and so on. We partnered with a nonprofit, Grow Movement, to recruit international professionals from more than 60 countries to engage in the volunteer activity.Graph: Figure 1. Timeline and data collection.When recruiting the professionals, Grow Movement did not focus on specific functional backgrounds; rather, the organization recruited volunteers from multiple areas with substantial business experience and time to work with an entrepreneur. Marketers made up the largest group: 26% of the volunteers. Business professionals from consulting and other functional backgrounds were also included. After being randomly assigned to the control group (n = 400) or the treatment group (n = 530), the entrepreneurs receiving the intervention were randomly matched with volunteers. The result was three exogenously determined groups of 136, 122, and 272 treated entrepreneurs working with volunteers from ""marketing,"" ""consulting,"" and ""other"" backgrounds, respectively. Each entrepreneur–volunteer pair worked virtually for two to six months to improve business performance.Our study shows the intervention was effective, especially for entrepreneurs collaborating with volunteer marketers. Compared with the control group, firms matched with volunteer marketers increased monthly sales by 51.7%. The firms also achieved 35.8% higher profits than control firms and increased total assets by 31.0% and employees by 23.8%. Importantly, based on a standardized outcome index, only the firms matched with volunteer marketers experienced significant firm growth compared with the control group.[ 6]Mechanism evidence suggests that the volunteer marketers tended to help entrepreneurs differentiate their businesses by focusing on the goods or services they offer.[ 7] A linguistic analysis of the meetings and interactions between volunteers and entrepreneurs indicates that the marketers spent significantly more time on product-related topics than volunteers from other functional areas. Moreover, an intermediate outcome analysis shows that entrepreneurs collaborating with volunteer marketers increased average product price, contribution, markup percentage, and value add compared with those in the control group, indicating that the firms offered more premium products after the intervention than before ([10]; [13]). In addition, we find that these premium product proxies (e.g., price) mediate volunteer marketers' effect on firm growth.We also investigated heterogeneous treatment effects. In particular, international volunteers are unlikely to have local-market knowledge, a prerequisite for developing business differentiation ([43]), and firms require resources to deploy differentiation efforts ([30]). Accordingly, our results show that emerging-market entrepreneurs with greater ex ante market knowledge or resource availability gain the most from working with a volunteer marketer.Our study is the first field experiment examining whether and how volunteer marketers help emerging-market entrepreneurs grow their businesses. By addressing our two research questions (i.e., the main effect and its mechanism), we add to the literature in marketing, entrepreneurship, and development economics. We advance understanding of the effectiveness of business support services, including new ways of designing virtual collaborations leveraging technology and enhancing access for emerging-market entrepreneurs. We hope the study assists organizations such as the United Nations and multinationals such as Unilever or Procter & Gamble in designing future business support services for emerging markets.While promoting firm growth by differentiating products is a core marketing tenet, establishing and quantifying marketing's causal impact on growth is nontrivial ([ 9]). Our study causally identifies marketers' positive impact on emerging-market entrepreneur firm growth, thereby adding to the entrepreneurship literature (e.g., [36]; [56]) and research on marketing's influence within the firm (e.g., [25]; [53]).In addition, while it may seem obvious that marketing professionals focus on differentiation and premium products, this approach may be counterintuitive in emerging markets, where consumers have limited disposable income. If emerging-market consumers can only afford inexpensive, low-quality products, premium products are likely to fail. Our study indicates that this assumption is incorrect. We show that emerging-market entrepreneurs can successfully offer premium products well-aligned with their customers' needs and wants. Thus, we provide support for [34] observation that low-income consumers in emerging markets desire premium products (see also [ 5]]). Our finding also responds to calls for research on how to operate in emerging markets ([42]).Finally, our heterogeneous treatment effects provide guidance on which emerging-market entrepreneurs marketing interventions should target (i.e., those with greater ex ante market knowledge or resource availability). Many economists believe that emerging-market entrepreneurs fail to flourish largely due to resource constraints (e.g., [58]). Our results confirm that more resources help. However, our results also suggest that emerging-market entrepreneurs may require guidance to use available resources effectively. Entrepreneurship in Sub-Saharan AfricaMany people in emerging markets start businesses ([22]). Due to limited employment opportunities, the businesses are typically necessity-driven, created for survival rather than to address a clearly identified market opportunity. Most of the businesses are small and undifferentiated and cannot grow beyond subsistence. Many emerging-market entrepreneurs' products closely resemble other products, making it difficult to succeed and grow ([ 6]). When emerging-market firms fail to grow, gainful employment and its positive effects also stagnate ([11]; [31]).All else equal, emerging-market entrepreneurs who operate growing businesses enjoy enhanced income and greater purchasing power. The entrepreneurs' families are able to afford quality food, education, and health care and are generally less concerned about meeting basic needs. Their employees benefit through increased wages and job stability. Stable jobs enable employees to access savings accounts and loans to purchase products such as stoves and refrigerators, which can significantly increase quality of life. Emerging-market governments and societies also benefit from growing entrepreneurial businesses, as the firms typically pay higher taxes, and the additional income can be used to enhance regulations and infrastructure (e.g., transportation, sewers, freshwater systems).Research has shown that entrepreneurship is one of the most effective means of alleviating poverty in emerging markets ([ 6]; [20]; [47]). Scholars also suggest that businesses must clearly identify opportunities in their markets and stand out from the crowd (i.e., be sufficiently differentiated) to grow ([ 6]; [31]). Differentiation opportunities abound in emerging markets (e.g., [38]), but entrepreneurs must identify and implement them. Unfortunately, significant gaps remain in emerging-market entrepreneurs' business education and knowledge quality and relevance ([ 2]; [ 8]; [31]; [38]).We suggest that, as a possible solution, experienced professionals could volunteer time to guide emerging-market entrepreneurs. Specifically, we suggest that virtually connecting emerging-market entrepreneurs with experienced professionals from advanced markets could facilitate differentiation. Given their functional backgrounds and experience, we believe volunteer marketers should be particularly effective for helping the entrepreneurs identify and implement viable differentiation strategies, as marketing helps firms discover market needs and customer groups, target appropriate customers, and position products so customers recognize them as distinct from others ([33], p. 5).A recent study by [ 3] examines how remote volunteers help emerging-market entrepreneurs ""pivot"" their business model (broadly defined; see [44]]), thereby helping them improve their firms' sales. That study is based on the same business support intervention and data gathering as our study. However, there are key distinctions between their study and ours. First, we focus on isolating the specific impact of marketing volunteers (vs. volunteers in general) as well as how marketing volunteers help emerging-market entrepreneurs become more differentiated by offering premium products. Neither of these aspects (i.e., main effect and mechanism differences) are considered in Anderson, Chintagunta, and Vilcassim. Second, we include multiple outcome measures (e.g., profits, assets, employees, firm growth indices) beyond just sales, which is the focal outcome considered in Anderson, Chintagunta, and Vilcassim. Third, our mediation and text analyses in support of the mechanism are unique and add further distinction. Fourth, our article's interaction analyses are novel given our use of multiple business-level moderators as well as our examination of nonlinear relationships. As a result, our study provides more fine-grained information for governments, nongovernmental organizations (NGOs), researchers, and multinationals on the types of businesses and volunteers likely to lead to greater differentiation and firm growth. The two studies should therefore be viewed as complementary. Volunteer Marketers and Emerging-Market EntrepreneursMarketing and entrepreneurship are two key responsibilities of any young firm ([17]). However, research on the combination and interaction of marketing and entrepreneurship is sparse (e.g., [36]; [39]; [56]) and suggests competing insights. [15] hints at incompatibilities between marketing and entrepreneurship, arguing that market-oriented entrepreneurial firms (i.e., those in which marketing flourishes [[32]]) fail to innovate because they are preoccupied with the market ([36]; [39]). In contrast, [56] argue that marketing significantly supports the entrepreneurship process (see also [36]). Although they do not test their predictions empirically, Webb et al. propose marketing activities and entrepreneurship processes are positively and reciprocally related. Marketing and the Entrepreneurship ProcessThe archetypal entrepreneurship process has five stages ([12]. The process begins with ( 1) entrepreneurial alertness, which leads to ( 2) recognizing an opportunity, followed by ( 3) innovation, ( 4) opportunity exploitation, and ( 5) enhanced performance. [56] propose that marketing—in particular an entrepreneurial firm's market orientation and marketing-mix skills—positively influences the five steps and enhances performance. The theory implicitly assumes that entrepreneurs, either themselves or through employees, have access to marketing capabilities. However, the assumption is less likely to apply to emerging-market entrepreneurs than those in advanced markets.Research has shown that emerging-market entrepreneurs employ ""sporadic and rudimentary"" marketing efforts ([38], p. 49) and lack marketing knowledge and related skills ([ 2]; [31]). Most emerging market entrepreneurial ventures have few employees ([38]), and the workforce cannot compensate for the entrepreneur's lack of marketing knowledge. Thus, emerging markets are less likely to experience the positive interaction between marketing and entrepreneurship that [56] propose. However, we argue that virtual access to professionals with marketing backgrounds could help emerging-market entrepreneurs address their capability gap. International Business Support from Volunteer MarketersExtant research indicates that emerging-market entrepreneurs can acquire general marketing capabilities by attending broad, in-class marketing courses ([ 2]). We propose that emerging-market entrepreneurs can also acquire the skills by collaborating with an experienced volunteer from an advanced market. In contrast to group-based marketing principles courses ([ 2]), one-on-one collaborations deal directly with each entrepreneur's unique products and business challenges. Thus, regularly interacting with an experienced volunteer marketer may be more applicable to entrepreneurs than general classroom training ([14]; [38]).Depending on their functional backgrounds, volunteers likely emphasize different business practices during their collaborations with entrepreneurs. Volunteers naturally bring their own experiences to interactions with entrepreneurs (e.g., [21]), and even when business professionals operate outside their primary functional area, past learning and conditioning affects their thinking ([55]) and leads them toward familiar solutions ([35]). Kaplan's Law states that individuals rely on familiar ""tools"" ([28]); thus, we expect volunteer marketers to focus on their marketing expertise during their interactions with entrepreneurs. Likewise, we expect volunteers with other backgrounds to focus on their unique skills.Marketing education and professional development emphasizes identifying demand-increasing opportunities (e.g., [19]; [57]). Most other business functions focus on throughput. The finance, legal, and accounting functions, for example, focus internally on improving firm efficiency ([23]). A significant body of research indicates that marketers recognize market-based opportunities (e.g., [54]; [59]) and help firms differentiate ([33], p. 5; [48]). Marketers say that they keep differentiation strategies at the top of their minds (e.g., [51]). Volunteer marketers should thus be well suited and eager to help emerging-market entrepreneurs differentiate and address one cause of their low growth rates ([ 6]). Therefore, we expect emerging-market entrepreneurs to exhibit improved performance and grow their firms after interacting with volunteer marketers.Firms often make product changes and attempt to align better with target customers' needs and wants to become more differentiated ([30], p. 628). Indeed, [43] argues that firms frequently aim to distinguish themselves from their rivals by offering differentiated products. Moreover, the emerging-market context makes it difficult for entrepreneurs to differentiate on characteristics other than product. That is, their businesses tend to be local, so differentiation tactics relying on adding new channels or advertising and promotion are less accessible. Thus, ceteris paribus, we expect volunteer marketers to focus on product-related differentiation during collaborations with emerging-market entrepreneurs.That said, firms can use several approaches to differentiate their products ([16]), and it is not clear, a priori, which tactic emerging-market entrepreneurs working with volunteer marketers would use. Therefore, we set up our experimental design and data collection so we could explore the approaches that entrepreneurs pursued. Study DesignStudying volunteer marketers' impact on emerging-market entrepreneurs' differentiation and growth is challenging. No databases record both firm growth indicators (e.g., sales) over time for the same set of entrepreneurs and the functional backgrounds of volunteer business professionals working with the entrepreneurs. Moreover, exogeneous variation in entrepreneur exposure to the volunteers would be needed to overcome omitted variables bias (e.g., unobserved alternative factors driving firm growth) and reverse-causality concerns (e.g., substantial firm size as a prerequisite for attracting assistance). In addition, obtaining a relevant panel data set may still not solve potential bias from self-selection by entrepreneurs (i.e., varying motivations for choosing to receive assistance) and volunteers (i.e., different preferences for choosing firms to work with). We therefore conducted a two-year field experiment (see Figure 1) in which 930 Ugandan entrepreneurs were randomized into a control group (n = 400) and a treatment group (n = 530). We also randomly matched the treated firms with volunteer business professionals from different functional backgrounds. Sample Recruitment and Preintervention Data CollectionFrom January to August 2015, we followed multiple steps to obtain a representative sample of emerging-market entrepreneurs running small firms in Uganda.[ 8] First, a team of 15 enumerators went door-to-door across greater Kampala, systematically covering all business hubs, marketplaces, and commercial zones. We conducted a recruitment survey of every entrepreneur who could speak conversational English, operated their firm from a physical structure, and was interested in receiving assistance from a volunteer business professional. The survey contained questions on entrepreneur and business characteristics for screening or to be used as controls in our main analysis. Our sampling frame includes the 4,043 entrepreneurs who completed the recruitment survey.We then implemented an ""established firm"" scorecard, ranging from 0 to 100 points, using nine proxies from the recruitment survey: business premises, upfront investment, full-time staff, internal affairs organization, new activities and processes, business and formal education, prior corporate experience, exposure to other countries, and external ecosystem awareness. We ranked the 4,043 entrepreneurs using the scorecard and proceeded with the top 1,500 firms.[ 9] We attempted a baseline survey of the entire group; however, only 1,254 entrepreneurs completed the 90-minute site visit and audit. The survey contained business background questions, detailed financial data (e.g., sales, profits, assets, employees), and product data (e.g., descriptions, prices, costs, markups). Finally, our partner invited the qualifying 1,254 entrepreneurs to a one-on-one interview where they received details about the business support service. Our partner used the registration meeting as an additional eligibility screen and approved 930 entrepreneurs, which formed our sample. The sample includes a broad mix of firms, with business-to-consumer retailers and service providers being the most common. (For a summary of firms by industry, see Web Appendix 1.) Randomization, Matching, and Functional BackgroundsAll 930 firms were randomly assigned to a control group (n = 400) or a treatment group (n = 530). Each treated firm was randomly matched one-to-one with a unique volunteer business professional. The randomization process was done by computer, so differences across groups were due to chance.Two independent experts coded volunteers' background variables after the study finished using their curriculum vitae, LinkedIn profiles, and partner administrative data. The coders did not have access to entrepreneur or firm data. Volunteers' primary functional backgrounds refer to the business area or specialization in which they spent the majority of their career until project participation. The interrater reliability for coding functional backgrounds was 89.8%; all discrepancies were resolved through discussion. Background data were missing or insufficient for 38 volunteers. The 530 functional backgrounds were coded into ten areas: marketing and sales (n = 136), consulting and advisory (n = 122), finance and accounting (n = 84), strategy and general management (n = 48), engineering and research and development (n = 39), operations and supply chain (n = 23), entrepreneurs and owners (n = 18), human resources (n = 14), legal (n = 8), and unknown (n = 38).All entrepreneurs and volunteers, as well as the partner's intervention managers, were blind to the experiment. We permitted no one to switch volunteers or entrepreneurs, and we controlled all matching steps and dyad formation. Thus, self-selection did not occur and the assignment of volunteers to treated firms was exogenously determined. This randomized matching (of volunteers and entrepreneurs) enabled us to construct treatment groups based on functional backgrounds. We set the group size minimum at 100 firms to provide sufficient statistical power and thus divided our study sample of 930 firms into four experimental groups: ( 1) treatment 1 (or marketers), which includes the 136 entrepreneurs exposed to a marketing/sales volunteer; ( 2) treatment 2 (or consultants), which includes the 122 entrepreneurs exposed to a consulting/advisory volunteer; ( 3) treatment 3 (or other professionals), which includes the 272 entrepreneurs exposed to volunteers from one of the remaining functional areas (e.g., finance, engineering, strategy, operations); and ( 4) control, which includes the 400 entrepreneurs who did not receive the intervention during the two-year study.The identification approach enables us to isolate marketing volunteers' effect on firm growth and product differentiation. It is aligned with our research objective of understanding the relationship between volunteer marketers and emerging-market entrepreneurs. Intervention Overview: Collaborating with VolunteersOur intervention exposed each Ugandan entrepreneur to a volunteer in a different country and let the dyad work together for two to six months to improve firm performance. The collaborations were virtual, with every entrepreneur–volunteer interaction, sometimes multiple per week, happening via Skype video conferencing, mobile calls, and text messages. Many dyads leveraged other virtual productivity tools, such as email, Google Docs, Dropbox, and WhatsApp. Our partner, Grow Movement, provided in-country intervention managers to facilitate introductions and ensure that collaborations continued on schedule but otherwise did not intervene. The partner maintained an online project management system allowing volunteers to enter goals, track milestones, and record interaction details at biweekly intervals. Outside its basic structure, the intervention was open-ended (i.e., the volunteers had the discretion to guide the project and tailor the topics, assignments, and activities as they saw fit). Web Appendix 2 provides examples of typical entrepreneurs in the sample and their products.The 530 volunteers approved to participate in the project initially applied online via the Grow Movement website. Our partner subsequently interviewed and vetted them to ensure we matched only committed volunteers with entrepreneurs. The volunteers had to demonstrate substantial business experience and convince Grow Movement they were willing to work with a Ugandan entrepreneur for multiple months to improve business performance. The partner did not implement prerequisites or quotas regarding volunteers' functional backgrounds. The intervention included business professionals from nearly every continent (see Web Appendixes 3 and 4). Volunteers represented more than 60 countries, with the largest number coming from the United Kingdom (28%), India (10%), the United States (9%), Germany (4%), Italy (4%), Canada (4%), Australia (3%), and Spain (3%). Intervention Strength and Compliance RatesThe intervention featured a relatively high take-up rate, as 88% of treated entrepreneurs completed at least one of the two-week modules, each of which included multiple interactions with a volunteer (for a breakdown by treatment group, see Web Appendix 5). The first two-week module entailed arranging logistics with an intervention manager, scheduling a two-hour Skype call with the matched volunteer, traveling to a field office or internet café to hold the call, completing multiple assignments (e.g., problem identification, product details, financials, market research, goal setting), and communicating with the professional via follow-up calls, texts, and emails. Intervention compliance was relatively high. The typical collaboration lasted about 2.5 months, with the average number of completed modules varying by group (marketers = 5.04, consultants = 5.98, other professionals = 5.60).[10] However, entrepreneurs reported completing more modules (around eight in total) than were recorded in our partner's system, likely making the compliance estimate a lower bound. Postintervention Data CollectionOur study's intervention phase lasted roughly one year, from August 2015 to July 2016. To allow a two-year gap for potential growth from pre- to postintervention data collection, we implemented our end-line survey in May 2017. An independent auditor conducted the survey at each entrepreneur's business location under the supervision of an Innovations for Poverty Action (IPA) research manager (the Uganda office of IPA hosted our study and provided research support). Questions closely mirrored those in the baseline survey to ensure that auditors collected the same financial data (e.g., sales, profits, assets, employees) and product differentiation data (e.g., descriptions, prices, costs, markups) pre- and postintervention. We used an electronic survey tool to collect firm financial data and followed a standard aggregation, anchoring, and adjustment methodology to obtain plausible and precise estimates on key outcomes such as sales and profits ([ 4]). Our team leaders, field manager, and research manager took several rigorous auditing and verification steps to ensure that every survey was complete and accurate.[11] Firm Growth MeasurementOur study aims to learn whether and how volunteer marketers help emerging-market entrepreneurs improve their business performance and size. Firm growth is the main outcome of interest. We define firm growth conceptually as an increase in a firm's sales, profits, assets, or employees. We measure firm growth operationally using several indicators and two overall indices. We use aided-recall and iterative anchored-adjusted approaches to measure monthly sales and profits ([ 4]). Drawing on these measures, we constructed four composites of monthly sales and profits: ( 1) a winsorized sales composite (average of the aided-recall and anchored-adjusted sales measures after winsorizing each 1%), ( 2) an inverse-hyperbolic-sine (IHS)-transformed sales composite (average of the aided-recall and anchored-adjusted sales measures after IHS-transforming each), ( 3) a winsorized profits composite (average of the aided-recall and anchored-adjusted profit measures after winsorizing each 1%), and ( 4) an IHS-transformed profits composite (average of the aided-recall and anchored-adjusted profit measures after IHS-transforming each). Moreover, we use an iterative approach to measure the current value of all firm assets and the number of employees, again constructing four composites: ( 1) a winsorized (1%) assets composite, ( 2) an IHS-transformed assets composite, ( 3) a winsorized (1%) employees composite, and ( 4) an IHS-transformed employees composite.Finally, we constructed two indices of firm growth. For the first index, we used the following 12 measures: ( 1) aided-recall sales winsorized, ( 2) anchored-adjusted sales winsorized, ( 3) aided-recall sales IHS-transformed, ( 4) anchored-adjusted sales IHS-transformed, ( 5) aided-recall profits winsorized, ( 6) anchored-adjusted profits winsorized, ( 7) aided-recall profits IHS-transformed, ( 8) anchored-adjusted profits IHS-transformed, ( 9) assets winsorized, (10) assets IHS-transformed, (11) employees winsorized, and (12) employees IHS-transformed. We standardized each of these 12 measures (control group as the base) and then computed the average of these values to construct the overall Firm Growth Index 1 outcome variable. For the second index, we used the following eight composite measures: ( 1) winsorized sales composite, ( 2) IHS-transformed sales composite, ( 3) winsorized profits composite, ( 4) IHS-transformed profits composite, ( 5) winsorized assets composite, ( 6) IHS-transformed assets composite, ( 7) winsorized employees composite, and ( 8) IHS-transformed employees composite. We again standardized each of these eight composite measures (control group as the base) and then computed the average of these values to construct the overall Firm Growth Index 2 outcome variable. This second index measure is the main dependent variable used in our additional analyses (i.e., intermediate effects and interaction effects). Combining the outcomes into an index better represents the construct by capturing all relevant dimensions, improving statistical power to detect effects in the same direction, and guarding against multiple hypothesis testing (e.g., [14]). Web Appendix 6 provides additional details for each firm growth indicator and index. Empirical Methodology and Summary Statistics Model SpecificationGiven that we randomly assigned entrepreneurs to experimental groups, we estimate the effect of exposure to a volunteer business professional as the difference in average outcomes for the treatment and control firms at end line using an intention-to-treat regression:Yi=α+β1Marketeri+β2Consultanti+β3OtherProfessionali+∑γsdi.s+δYi, b+∊i.1Yi is the dependent variable (i.e., firm growth) for firm i at end line. Marketeri is a treatment dummy variable indicating whether a firm is randomly assigned to the marketing intervention and matched with a marketing volunteer. Consultanti is a treatment dummy variable indicating whether a firm is randomized into the consultant intervention group and matched with a consulting volunteer. OtherProfessionali is a treatment dummy variable indicating whether a firm is randomized into the other professional intervention group and matched with a nonmarketing or nonconsulting volunteer.[12] di.s comprises control variables measured preintervention, including 10 controls for baseline entrepreneur characteristics (gender, age, ethnicity, marital status, children, education level, business program, prior salaried job, previous ownership experience, and commitment), 15 controls for baseline business characteristics (founder, operating years, start-up capital, formal loans, separation of business–personal affairs, days open per week, sales frequency, business premises, location, registration, size, business practices, product competition, business-to-business customers, and markets outside neighborhood), and 10 industry fixed effects based on two-digit Standard Industrial Classification codes. We include the controls to improve estimate precision and account for any group imbalances due to attrition or spurious correlations in interaction analyses. Equation 1 also controls for the baseline value of the dependent variable, Yi, b (whenever this outcome was measured at baseline).[13] Robust standard errors are reported in all regression specifications. Because the dependent variable is continuous (e.g., sales, profits, assets, employees), we estimate Equation 1 via an ordinary least squares regression. Firm and Entrepreneur ProfileIn our sample, 70% of the firms are run by the founder and, on average, have been in operation for nearly four years and are open 6.5 days per week. The firms are fairly formalized, with 74% maintaining separate business and personal affairs, 13% having received a financial institution loan, and 22% being formally government-registered. The average firm in the sample operates from a small stand-alone shop or larger physical premises, is located in a busy area, has monthly sales of 4.4 million UGX (∼$1,190[14]), has monthly profits of 673,000 UGX (∼$184), owns assets valued at 14.4 million UGX (∼$3,950), and employs 1.7 paid staff (excluding the owner).Female entrepreneurs make up 40% of the sample, and 99% are local Ugandans. The typical entrepreneur is 31 years old, has 2.3 children, and has completed at least high school. On average, 55% have engaged in a prior business development program (e.g., training course), 54% are married, and 46% previously owned a business. Web Appendix 7 displays summary baseline statistics for our full sample of 930 firms. Balance ChecksOur experimental groups are reasonably balanced on preintervention covariates (i.e., randomization was successful; see Web Appendix 7). Out of 120 t-tests, we find six statistically significant differences in means, which would be expected by chance. Nonetheless, we control for entrepreneur and business characteristics in all regression analyses to account for group imbalances on observables.[15] We perform attrition and survival checks but do not detect differential effects among groups (see Web Appendix 9).Given that the experimental groups do not differ in attrition or failure, our subsequent analysis includes the full sample of survivors with complete end-line surveys and key data (n = 605). We also followed the standard conservative approach for dealing with nonsurvivors in small firm studies suggested by [ 2] and rerun each analysis with nonsurvivors, obtaining qualitatively similar results. Main Effect: Analysis and Results Model Free Evidence for Volunteer Marketers' ImpactFigure 2 provides model-free evidence for volunteer marketers' impact on firm growth. The control group decreased on the raw index measure (−.030 SD) from baseline to end line. The average change in growth for the marketer treatment group is positive (.123 SD) and significantly larger than for the control group (p =.042). We see a similar pattern of positive growth effects across our outcome measures: change in monthly sales, monthly profits, total assets, and paid employees is greater for firms exposed to a volunteer marketer than for control firms. We also plotted the four experimental groups' cumulative distribution functions for the firm growth index, which show a rightward shift for treated firms. In particular, across the distribution, it appears that entrepreneurs matched with a volunteer marketer achieved the most growth compared with the control group (see Web Appendix 10).Graph: Figure 2. Volunteer marketers' main effects on firm growth.*p <.10.**p <.05.Notes: The y-axis represents the pre-to-post change in Firm Growth Index 2. Error bars = ±1 SE. Regression Results for Volunteer Marketers' ImpactTable 1 presents our regression results for the volunteers' effect on firm growth. Our findings from the intention-to-treat analysis are consistent with the model free evidence. Across the outcome measures, we see significant positive main effects for the marketer treatment group (for full details, see Web Appendix 11).GraphTable 1. Volunteer Marketers' Main Effects on Firm Growth. 40022242921993176 *p <.10.50022242921993176 **p <.05.60022242921993176 ***p <.01.70022242921993176 Notes: Robust standard errors are in parentheses. Firm growth values in levels (sales, profits, assets) are listed as Ugandan Shillings (UGX) in thousands. Firm Growth Index 1 is the average of the 12 standardized measures of sales, profits, assets, and employees. Firm Growth Index 2 is the average of the eight standardized composites of sales, profits, assets, and employees.We find that entrepreneurs who were matched with a volunteer marketer, on average, increased in size on multiple growth indicators. Table 1 shows monthly sales increased by 2,311,757 UGX (51.7% or.30 SD), monthly profits by 292,912 UGX (35.8% or.23 SD), total assets by 4,386,521 UGX (31.0% or.19 SD), and paid employees by.45 (23.8% or.17 SD) for marketer treatment group firms compared with control group firms. We also include the respective changes in logs (based on the IHS-transformed measures) in Table 1 for each growth indicator. Although firm growth measures commonly feature large standard errors in emerging market business studies ([37]; [38]), we find consistent coefficient magnitudes across our eight indicators (32.5% average effect size across columns 1–8 of Table 1).Most importantly, our overall firm growth indices are positive and significant. Table 1 shows a firm growth index effect of.187 to.189 standard deviations for volunteer marketers, 2.95 times greater than that for consultants (.064 SD) and 2.49 times greater than that for other professionals (.076 SD). Taken together, the regression analysis finds a positive and meaningful treatment effect for the marketing intervention. For example, a 292,912 UGX ($80) increase in monthly profits (i.e., the marketer treatment effect in column 3) would enable the average firm in our sample to substantially expand its business premises, especially given that mean rent at baseline was 341,136 UGX per month. Moreover, as per Table 1 (column 5), growing total assets by 4,386,521 UGX ($1,200) is equivalent to a 67% rise in stock and inventory. Such working capital gains can fuel the sales engine of a small emerging-market business. Overall, the main-effect results suggest that entrepreneurs exposed to a marketer tended to grow their firms more than those who did not receive any intervention.[16] Robustness 1We obtain a similar pattern of main effect results using the following alternative specifications: excluding control variables, selecting control variables via Lasso, including nonsurvivor firms, and designating the marketer treatment as the excluded base group. The main effect also continues to hold when we use difference-in-differences approaches instead of the analysis of covariance model specified in Equation 1. We further support our findings using a bounding exercise to examine attrition, where lost control group firms are assigned the treated firms' average growth values. Web Appendix 12 shows these robustness checks. Robustness 2Web Appendix 13 presents additional robustness checks. The regression results show that the marketer treatment effects continue to hold, with coefficients similar to those in Table 1, when consultants and other professionals are collapsed into a single treatment group labeled nonmarketers. Critically, this lends support to the exogeneity of the marketer treatment dummy (i.e., the randomized matching of entrepreneurs and volunteers) as the effects remain similar. The nonmarketer treatment dummy variable is significant for the sales outcomes, which is consistent with [ 3] findings. Mechanism: Analysis and ResultsWe argued that volunteer marketers help emerging-market entrepreneurs differentiate, a trait that many entrepreneurs lack and a key reason that they fail or stagnate ([ 6]). Moreover, we predicted that volunteer marketers would focus specifically on product-related differentiation strategies. However, we noted that firms can take different routes to product differentiation ([16]), and it is not clear how the entrepreneurs exposed to volunteer marketers would proceed. Thus, we set up our experimental design and data collection so we could analyze the entrepreneurs' approaches. In what follows, we present the insights from these analyses. Intervention Effects: Insights from Linguistic AnalysisAs we have described, the volunteers were encouraged to use Grow Movement's online project management system to summarize the topics they discussed in each entrepreneur meeting. All summaries were provided in English and saved in the partner's database. On average, 71.5% of volunteer marketers, 70.6% of volunteer consultants, and 69.1% of other volunteers provided written summaries. The entry rates were not significantly different (p >.55). We also examined average entry length; marketers averaged 959 words (SD = 1,413), consultants averaged 1,163 words (SD = 1,518), and other professionals averaged 915 words (SD = 1,380). The three groups did not significantly differ in average words used (p >.18).Words and text provide information about their author ([52]), and analysts can aggregate text across authors to study larger groups. Because grouping individuals on the basis of shared characteristics can provide insight into their similarities and differences ([ 7]), we first organized all session summary text by treatment group. We then used topic modeling to identify underlying themes and general topics discussed during the intervention and differences in the extent to which each treatment group focused on topics. We used structural topic modeling (STM) for the analysis, removing stop words and employing stemming ([ 7]). We also removed all names. We employed the ""stm: R package"" developed by [45] for our analysis. No clear guidance is available for selecting an optimal number of topics for STM analysis ([ 7]). However, the semantic coherence measure of our data was highest when we set topics at K = 6. Thus, combining the statistical measure results with researcher judgment ([ 7]), we used K = 6 topics. Table 2 presents the topics extracted from the text, along with the words most likely to be present for each ([45]).GraphTable 2. Linguistic Analysis Insights. 80022242921993180 *p <.10.90022242921993180 **p <.05.100022242921993180 ***p <.01.110022242921993180 Notes: Marketers devoted significantly more text to topic 4 (18%) than consultants (10%) and the other professionals (12%). In addition, marketers devoted significantly less text to topic 6 (6%) than the other professionals (10%). Text devoted to the six topics does not significantly differ between the consultants and other professionals.Across the three treatment groups, volunteers devoted similar amounts of text to the six topics when creating their session summaries, with one notable exception. Volunteer marketers devoted significantly more text to topic 4, which relates to products, than consultants and other professionals. Topic 4 captures text such as ""She has a good handle on the profit and loss side of business. To grow the business [she] will need to focus on marketing [her products better]"" and ""She has visited three supermarkets [so far]. They are telling her that they want her product delivered hot and have their own display."" Other text includes ""Are there products that are often wasted and not sold?,"" ""Are there products that take a lot of time to make?,"" and ""[I advised her to] introduce a new line of products.""In particular, volunteer marketers devoted, on average, 18% of their text to topic 4. Consulting volunteers devoted, on average, 10% of their text to topic 4, while other volunteers devoted, on average, 12% to topic 4. The differences are statistically significant, with marketers being greater than consultants (p =.006) and than other volunteers (p =.015).Topic 4 captures text devoted to products, including their performance, which resonates with customers. Thus, consistent with our prediction, our STM results suggest that volunteer marketers aimed to help entrepreneurs differentiate through product-focused approaches. However, the STM results do not offer insights into how entrepreneurs supported by volunteer marketers changed their products. Nevertheless, these results indicate further analyses pertaining to emerging-market entrepreneurs' products are warranted.It is also noteworthy that marketers did not devote more text to topic 2 (which captures text devoted to customers and the market) than the other volunteers. This finding suggests that customer and market-related topics—aside from product-related discussions captured by topic 4—were equally covered across the treatment groups. Thus, this offers further evidence that volunteer marketers' product focus was a key driver of their positive impact, again suggesting that additional analyses of emerging-market entrepreneurs' products are worthwhile. Intermediate Effects: Insights from Mediation AnalysisAccording to [43], to effectively differentiate products, firms must provide some unique and meaningful value. [43] also argues firms that differentiate are frequently able to charge a premium price for their products, not just to compensate for potentially higher costs but also to achieve higher margins. Notably, differentiation has been found to reduce customers' price sensitivity and to enable the firm to earn a price premium (e.g., [48]). That said, emerging-market entrepreneurs may also try to differentiate their products by offering lower prices (e.g., [ 5]). Against this backdrop, we analyzed the marketers' effect on four proxies to assess whether and how entrepreneurs differentiate their products: ( 1) price per unit, ( 2) contribution per unit, ( 3) markup percentage per unit, and ( 4) enhancement of products. Web Appendix 14 provides details on measurement of the product differentiation proxies. The regression results in Table 3 demonstrate volunteer marketers' impact on emerging-market entrepreneurs' product differentiation efforts (for full details, see Web Appendix 15).GraphTable 3. Volunteer Marketers' Effects on (Intermediate) Product-Related Outcomes. 120022242921993180 *p <.10.130022242921993180 **p <.05.140022242921993180 ***p <.01.150022242921993180 Notes: Columns 1–5 show the effects on different measures of product outcomes. Columns 6–10 show the relationship between each product measure and overall firm growth outcomes (i.e., Firm Growth Index 2, the average of the eight standardized composites of sales, profits, assets, and employees). Robust standard errors are in parentheses. Firm growth values in levels (price and contribution per unit) are listed as Ugandan shillings (UGX) in thousands.We find a 58.2% increase (β1 = 46.94) in average price per unit for firms in the marketer treatment group versus the control group. Moreover, we find that the average unit contribution increased by 75.2% (β1 = 23.36) for firms exposed to volunteer marketers relative to firms receiving no intervention. In addition, compared with control group firms, marketer treatment group firms improved markups by 15.3% on average, and 33.3% more of the firms (β1 =.103) enhanced their products. These results suggest that volunteer marketers indeed helped emerging-market entrepreneurs differentiate their products. The results also suggest that emerging-market entrepreneurs started offering more premium products—defined as products that demand ""higher prices"" and that ""provide greater value to consumers"" (e.g., [13])—after the marketing intervention. We also examined volunteer marketers' impact on changes in outcomes not related to product differentiation (e.g., firm operational or financial capabilities) but do not find significant effects, providing some evidence against alternative mechanism explanations.To address noisy measurement issues, we also tested volunteer marketers' effect on a product index (referred to as ""premium product index""), constructed by averaging the four standardized product differentiation proxies. As Table 3 shows, marketer group firms achieve a.254-standard-deviation increase for the overall premium product index compared with those in the control group, a roughly 37% increase. By contrast, we observe no significant change in the premium product index or the four product differentiation proxies for consultant and other professional group firms.In terms of the substantive impact for entrepreneurs who were paired with a volunteer marketer, on average, their per-unit prices increased by 46,944 UGX ($12.84, or a 58.2% increase relative to control firms), and their unit contribution increased by 23,356 UGX ($6.39, or a 75.2% increase relative to control firms). These increases represent meaningful effect sizes for entrepreneurs selling in a marketplace where most customers are earning $5–$12 per day.We next examined the relationship between the product differentiation proxies and firm growth. The general pattern of results suggests a positive and significant correlation between product differentiation and firm growth (see Table 3). We also tested whether product differentiation mediates volunteer marketers' effect on firm growth using [24] PROCESS Model 4. The indirect effect of the marketer treatment on our main firm growth index—through the premium product index—is positive and significant (i.e., a × b =.04; 95% confidence interval based on 10,000 bootstrap samples = [.01,.08]; see Web Appendix 16). Thus, entrepreneurs exposed to volunteer marketers not only created more premium products with higher prices, unit contributions, and markups but also were successful at selling these products, as indicated by their increased sales and profits. We repeated the mediation analysis for the consultant treatment and other professional treatment groups. Neither of the indirect effects was significant, indicating that product differentiation does not mediate the firm growth effects for these groups.Taken together, the results support our predictions that volunteer marketers help emerging-market entrepreneurs improve product differentiation. Interestingly, the focus seems to be on selling more ""premium"" products, which is somewhat counterintuitive given the low disposable incomes of consumers in these markets. This analysis uncovers at least one (new) process through which the marketing intervention leads to firm growth. Heterogeneous Effects: Analysis and ResultsNext, we analyzed interaction effects to determine which types of firms volunteer marketers help most. In particular, given the findings from the mechanism analysis thus far, the marketing intervention should be more effective for businesses better equipped for product differentiation. This raises the question, what makes a firm better equipped for a product differentiation–focused marketing intervention? [40] show that a firm's marketing capabilities and market orientation combine and interact and are akin to interconnected assets ([50]). Intelligence generation and dissemination are key components of a firm's market orientation ([27]). In turn, market knowledge is an important outcome of the two and helps firms understand customer preferences and competitor positions, which should enhance differentiated product development. Thus, we expect entrepreneurs with greater market knowledge to benefit more from the marketing intervention.Moreover, the marketing intervention enables and is akin to benchmarking (e.g., [54]), a learning process by which the entrepreneur tries to identify best practices from the volunteer marketer. That said, the benchmarking literature has shown that firms with greater resources are better equipped to act on benchmarking insights (e.g., [ 1]). Indeed, greater resources (e.g., money, time) should assist firms in delivering products to market and improve their deployment of premium, differentiated products. Thus, we expect entrepreneurs with greater resources to benefit more from the marketing intervention.We used three business characteristics to capture each firm's market knowledge (i.e., local market experience, demand tracking system, and diverse customers) and resource availability (i.e., start-up capital, business partners, and cash reserves). We provide details on measuring the characteristics in Web Appendixes 17 and 18. We created two composites for each construct (normalized 0–1 and median split, with 0 = lower and 1 = higher) and separately examined heterogeneity in the volunteer marketers' treatment effect.Table 4 (columns 1–5) presents interaction regressions based on a firm's ex ante market knowledge using the composite measures and all three dimensions. We observe positive firm growth effects for entrepreneurs exposed to volunteer marketers when the businesses have greater market knowledge. In particular, the marketer interaction coefficient is large, with a 2.71-standard-deviation firm growth increase. Interpreted differently, a 33% market knowledge composite increase (i.e., obtaining the maximum score on one of three dimensions) leads to a.904-standard-deviation gain in overall firm growth.GraphTable 4. Heterogeneity in Volunteer Marketers' Interaction Effects on Firm Growth. 160022242921993180 *p <.10.170022242921993180 **p <.05.180022242921993180 ***p <.01.190022242921993180 a Normalized 0–1; mean-centered.200022242921993180 Notes: MK = market knowledge; RA = resource availability. 15 business controls, 10 entrepreneur controls, and 10 industry fixed effects are included in all regressions. To avoid duplication, the ""start-up capital"" control is dropped from the resource availability regressions in columns 6–8. Robust standard errors are in parentheses. Firm Growth Index 2 is the average of the eight standardized composites of sales, profits, assets, and employees.Likewise, marketers' impact on firm growth is greater for entrepreneurs with more resource availability. As shown in Table 4 (columns 6–10), firms matched with volunteer marketers realize a 3.57-standard-deviation gain when their resource availability is highest (i.e., 1 on the normalized composite). The positive firm growth effects persist whether the composite measure is normalized or split at the median, as well as for each of its three dimensions. We note that when all interaction terms are included in the same model (column 11 in Table 4), the results are substantively similar.[17] Market Knowledge and Nonlinear Firm Growth EffectsWe also explored nonlinearities in the relationship between market knowledge and firm growth to delve deeper into heterogeneity. Web Appendix 19 summarizes the regression results when we include the continuous market knowledge measure (normalized 0–1 and mean-centered) and its squared term interacted with our treatment dummy variables. The positive impact on firm growth persists when businesses increase in market knowledge and are matched with a volunteer marketer. Moreover, we find a positive and significant squared term (7.03), which suggests that the relationship is nonlinear. We plot the predicted values from the regression in Figure 3 to highlight differences between the marketing treatment and control groups. For marketing treatment firms, we observe a convex relationship as market knowledge increases from the left tail (−.159) to the right tail (+.292) of its distribution. The plot shows that most of the interaction effect occurs toward the right tail, where market knowledge is highest and separation from the control group distribution is greatest.Graph: Figure 3. Market knowledge and nonlinear firm growth effects.Notes: The predicted values of firm growth (p-hat) are obtained following a nonlinear interaction analysis that regresses Firm Growth Index 2 onto the continuous measures of market knowledge and its squared term as well as the interactions of both variables with each of the treatment dummies (and the full set of controls). For complete results, see Web Appendix 19. For display purposes, 2.5% of the distribution's right tail is truncated in the figure. Error bars = ±1 SE.To better understand the pattern, we also divided the market knowledge composite into terciles and see a similar nonlinear relationship (see Web Appendix 19). Thus, these results suggest that only businesses with high market knowledge appear to see a large and increasing positive effect on firm growth when exposed to a marketer. Resource Availability and Nonlinear Firm Growth EffectsWe also explored treatment heterogeneity and nonlinear firm growth effects for resource availability. Web Appendix 20 summarizes the regression results when we include the continuous resource availability measure (normalized 0–1 and mean centered) and its squared term interacted with our treatment dummy variables. The positive firm growth effect persists when businesses increase in resource availability and are matched with a marketer. However, the negative and significant squared term (−14.19) suggests that the relationship is again nonlinear. We plot the predicted values from the regression in Figure 4 to highlight the differences between the marketing treatment and control groups. For marketing treatment firms, we observe a concave relationship as resource availability increases from the left tail (−.057) to the right tail (+.291) of its distribution. The plot shows that the interaction effect occurs mainly toward the mid- to right tail as resource availability increases.Graph: Figure 4. Resource availability and nonlinear firm growth effects.Notes: The predicted values of firm growth (p-hat) are obtained following a nonlinear interaction analysis that regresses Firm Growth Index 2 onto the continuous measures of resource availability and its squared term as well as the interactions of both variables with each of the treatment dummies (and the full set of controls). For complete results, see Web Appendix 20. For display purposes, 2.5% of the distribution's right tail is truncated in the figure. Error bars = ±1 SE.To further examine the nonlinear relationship, we also divided the resource availability composite into terciles and again obtain similar results (see Web Appendix 20). These findings indicate that only businesses with high resource availability appear to see a large and slightly decreasing positive effect on growth when exposed to a marketer. Discussion and ConclusionInterest in the effects of business support interventions on firm and economic growth in emerging markets has risen over the past decade. Researchers have suggested that entrepreneurship, in particular, can be a catalyst for growth ([14]; [20]). However, scholars have also pointed out a need for research determining which business skills are impactful, and for whom, and for work examining the process through which interventions enhance firm performance (e.g., [38]).Our results, based on a randomized controlled field experiment with 930 entrepreneurs in Uganda, indicate that volunteer marketers significantly and positively impact the entrepreneurs' firm growth by 32.5% on average, as measured in monthly sales and profits, total assets, and paid employees.Our theory and mechanism analyses indicate that volunteer marketers are effective because they help the entrepreneurs differentiate, a capability many desperately lack ([ 6]). Process evidence suggests that entrepreneurs matched with volunteer marketers create more premium products that resonate with target customers. Finally, our evidence based on interaction effects provides insight into which types of businesses benefit most from a volunteer marketer—namely, those with greater ex ante market knowledge or resources. Implications for Governmental Organizations and NGOsGovernmental organizations and NGOs invest billions in business support interventions to fight poverty in emerging markets each year ([14]). Researchers debate whether the aid is beneficial (e.g., [18]; [46]; [49]). Our study focuses on a basic, concrete question: Can marketers help small-scale entrepreneurs in Uganda grow their businesses? If yes, marketers could partially alleviate Uganda's pervasive poverty (e.g., [31]). As [20], p. 196) point out, ""Increasing the...quality of entrepreneurs is probably one of the most helpful ways to reduce poverty because it creates employment and boosts the innovation and economic empowerment of individuals in poor countries with extremely high unemployment rates.""Many emerging-market entrepreneurs struggle and fail to grow because they are ""utterly undifferentiated"" ([ 6]). We find that marketers can be especially effective as volunteers because they help entrepreneurs differentiate.We therefore offer governmental organizations and NGOs an accessible recommendation for future business support interventions in emerging markets. We hope our findings will earn marketers a seat at the policy table with organizations such as the World Bank, International Monetary Fund, and United Nations, which invest heavily in business and entrepreneurship programs every year. Our results suggest that the organizations should consider how marketers and marketing tools can be integrated into solutions for stimulating firm growth.Many economists believe that emerging-market entrepreneurs often fail to thrive due to resource constraints (e.g., [58]). While our results confirm that resources help entrepreneurs succeed, we find that resources alone may not be enough. Emerging-market entrepreneurs may also need guidance from experienced business professionals, particularly marketers, to use their available resources.Our partner, Grow Movement, estimates that each of its entrepreneur–volunteer collaborations costs $450–$500 when run at a large scale in a single country, where fixed costs can be spread across units. These costs compare favorably to other business support interventions in emerging markets (e.g., [14]; [38]), suggesting that governmental organizations and NGOs would be willing to support the costs. In fact, several business schools and NGOs have recently started incorporating versions of our ""remote coaching"" intervention into their programs with a focus on matching entrepreneurs with marketing practitioners. In addition, multinationals in developed markets could participate in future remote marketing coaching interventions such as ours. In short, we envision multinationals enabling their interested marketers to spend a few hours a week remotely coaching an emerging-market entrepreneur. This endeavor, we believe, could be a win-win for the entrepreneurs and the multinationals: the entrepreneurs' businesses would likely grow, and the multinationals would likely have more satisfied employees, accrue corporate social responsibility–related benefits, and learn about opportunities (and threats) in emerging markets. Implications for Emerging-Market Entrepreneurs and MarketersThe marketing literature has largely neglected entrepreneurial firms, which is surprising given the important role such companies play across all markets (e.g., [36]; [56]). Likewise, the entrepreneurship literature has largely ignored marketing, which is equally surprising, as some have argued that ""marketing is the home for the entrepreneurial process"" ([41], p. 247). Although marketing and entrepreneurship are two key business responsibilities ([17]), researchers have done little to understand how the two interact ([56]). Our study offers evidence that marketing and entrepreneurship blend especially well in emerging markets. The insight adds to the literature on marketing's influence within the firm (e.g., [25]; [53]), suggesting that emerging-market entrepreneurs benefit from marketing knowledge and skills.We hope that entrepreneurs in emerging markets take note of our findings and consider either acquiring marketing skills or hiring marketers. Marketers could consider partnering with entrepreneurial firms as volunteers or paid employees. Finally, we hope that emerging-market entrepreneurs and marketers note our finding that premium products can be successful in emerging markets. Thus, we add to the emerging literature on low-income consumers' preferences in emerging markets (e.g., [ 5]; [34]). Limitations and Future ResearchOur study is not without limitations, some of which provide opportunities for future research. Although our study was conducted over two years, longer than many prior business-support-intervention studies, its long-term implications are not obvious. For example, we cannot say with certainty that the treated entrepreneurs will continue using the marketing capabilities they acquired during the intervention. Although we show that the entrepreneurs significantly changed their products, which bodes well for long-term effects ([38]), future intervention studies might measure outcomes over longer periods.We randomly assigned volunteers to entrepreneurs as part of our experimental setup. Thus, we did not match volunteers and entrepreneurs on the basis of their backgrounds. However, more technical businesses, for example, might benefit from a volunteer with an engineering background. Entrepreneurs and volunteers might also match well on the basis of demographics such as gender or age. Future research should explore matching-related questions.Finally, some economists (e.g., [18]) and organizations (e.g., the American Enterprise Institute) are skeptical of or oppose foreign aid. Some suggest that foreign aid is often focused on recipients' material well-being without addressing underlying issues such as corrupt governments and individual rights suppression. These concerns are serious and valid; however, evidence suggests that flourishing entrepreneurship translates to positive long-term net effects in developing economies (e.g., [20]). We hope future research continues to explore ways in which marketers can play a role in ""doing good"" in the economies and societies of emerging markets, thereby contributing to a better world. " 17,Do Nudges Reduce Disparities? Choice Architecture Compensates for Low Consumer Knowledge," Choice architecture tools, commonly known as nudges, powerfully impact decisions and can improve welfare. Yet it is unclear who is most impacted by nudges. If nudge effects are moderated by socioeconomic status (SES), these differential effects could increase or decrease disparities across consumers. Using field data and several preregistered studies, the authors demonstrate that consumers with lower SES, domain knowledge, and numerical ability are impacted more by a wide variety of nudges. As a result, ""good nudges"" designed to increase selection of superior options reduced choice disparities, improving choices more among consumers with lower SES, lower financial literacy, and lower numeracy than among those with higher levels of these variables. Compared with ""good nudges,"" ""bad nudges"" designed to facilitate selection of inferior options exacerbated choice disparities. These results generalized across real retirement decisions, different nudges, and different decision domains. Across studies, the authors tested different explanations of why SES, domain knowledge, and numeracy moderate nudges. The results suggest that nudges are a useful tool for those who wish to reduce disparities. The research concludes with a discussion of implications for marketing firms and segmentation.","Choice architecture can powerfully impact decisions and improve welfare. Firms have adopted choice architecture changes that have increased retirement savings, increased environmentally friendly purchases, increased the number of premium features consumers buy when purchasing an automobile, and influenced other types of consumer decisions ([17]; [27]; [40]; [75]).But who does choice architecture influence most? Do changes to the choice environment impact some consumers more than others? We examined two related sources of heterogeneity in nudge effects, testing whether domain-specific skills and knowledge moderate nudge effects and whether socioeconomic status (SES) moderates nudge effects. We hypothesized that choice architecture can reduce choice disparities by having the largest impact on consumers with low SES and low levels of domain knowledge and skill.[ 6] Though choice architecture is inherent to online retail, many firms might not consider how choice architecture tools impact different consumers to different degrees, potentially reducing or exacerbating inequities between them. Knowledge of factors that make consumers more susceptible to choice architecture effects can allow firms and policy makers to use choice architecture more effectively to achieve the impact they want ([74]). Choice ArchitectureChoice architecture describes how a change in the structure of a choice influences behavior without significantly altering economic incentives or what consumers know about each option ([40]; [75]). Choice architecture can be manipulated to change the decisions that consumers make; these manipulations are often called ""nudges"" ([45]; [75]).Nudges are inexpensive and cost effective for firms and governments ([ 7]). Perhaps for this reason, they have gained tremendous popularity among policy makers and marketers ([ 2]; [ 7]). Over 200 ""nudge units"" currently exist around the world across private and public sectors ([ 2]). Marketing research has examined how choice architecture tools such as defaults and sorting alter consumer behavior (e.g., [22]; [27]; [40]). All marketing managers must make decisions about choice architecture. For example, retailers choose which products to display first and whether to use defaults to automatically select a shipping option, insurance, or product add-on ([72]; [75]). These decisions impact purchases and consumers' subsequent wealth, health, and well-being.Choice architecture is often used to facilitate choices that benefit consumers, firms, or both. For example, one auto manufacturer benefited both consumers and itself by changing the default car specifications on their website. Though it had previously defaulted consumers into basic, stripped-down car models, it found that changing defaults to tailor them to different types of customers increased firm profits while also benefiting consumers ([27]). Though nudges are frequently designed to help consumers, they can sometimes increase firm profits while decreasing consumer welfare. Nudges that harm consumers have been referred to as ""bad nudges,"" ""dark patterns,"" or ""evil nudges"" ([30]; [50]; [73]), in contrast to ""good nudges"" that benefit consumers. We examine whether ""bad nudges"" exacerbate choice disparities relative to ""good nudges"" by impacting low-SES and low-knowledge consumers most.There are many types of nudges, including defaults, sorting, partitioning, and several nudges that reduce the complexity or number of attributes or options ([15]; [16]; [20]; [22]; [39]; [40]; [49]; [70]). We examine three types of choice architecture: defaults, sorting, and changes to the number of options. Defaults, a type of nudge that preselects one option but allows consumers to easily opt out of the preselected option, have been called ""unquestionably the most prominent...[nudge], across all domains of application"" ([45], p. 27). Another nudge, called sorting, organizes options in a systematic way. For example, many products can be sorted by price, consumer rating, total cost, sales volume, or other attributes ([22]; [42]; [49]). Another form of choice architecture, which reduces the number of options presented to consumers, can improve decision making, reduce regret, and decrease the likelihood that consumers will defer their decision by choosing nothing ([10]; [16]; [40]). Who Gets Nudged?Previous research on nudges has typically focused on the overall effect of a nudge averaged across all individuals. For example, preselecting cars with premium features as the default increased the automobile purchase price by $1,500 on average ([27]) and opting people into retirement contributions resulted in large overall effects on enrollment ([17]). Other investigations have focused on the average cost effectiveness of nudges ([ 7]) or the impact of other nudges (e.g., sorting or changing the number of options) on the average consumer (e.g., [49]; [64]). Though these nudges have large impacts on average, it is unclear who they benefit most or whether they reduce or exacerbate inequities across consumers.Yet it is important to consider the heterogeneous impact of nudges rather than only the average effect collapsing across all consumers. Some scholars have suggested that nudges may affect the rich more than the poor ([61]). [61] argues that because structural factors impede the autonomy of vulnerable low-SES consumers, high-SES consumers will change their behavior when nudged, whereas low-SES consumers will be ""nudge-proof"" due to their lack of autonomy. A different prominent account suggests that scarcity and low income influence decision making by increasing time and attention on a focal task at the expense of tasks and decisions that are secondary or require thinking about the future ([69]). This might reduce the effect of nudges if heightened time and attention on focal decisions increase motivation and accuracy. In contrast, we predicted that nudges would impact consumers with low SES and less domain-specific knowledge and skills more than those with higher levels of these characteristics for other reasons (detailed subsequently and in Figure 1). Thus, we hypothesized that interventions encouraging the selection of the best option should reduce choice disparities between consumers who differ in SES, domain knowledge, and numeracy. We tested these predictions across a wide variety of contexts and nudges.Graph: Figure 1. Diagram of our theoretical framework explaining who is more susceptible to choice architecture and why.Notes: Consumers lower in SES and choice-relevant skills (e.g., numeracy) are impacted more by nudges. The model suggests that the SES moderator is explained by choice-relevant skills and knowledge, which moderates nudge effects partly because of anxiety, preference construction, and decision uncertainty. The relationships depicted by the dark gray arrows were the key relationships in our conceptual framework that we examined in primary analyses, and the light gray arrows were also supported by our data.We focused on the moderators of SES, numeracy, and domain knowledge for several reasons. We focused on SES partly because it is easy to measure and use for segmentation ([11]; firms often have this information about their customers) and partly because SES strongly influences consumer behavior ([13]; [23]; [34]). We also focused on SES because previous research on choice architecture has largely neglected how effects of nudges differ across different levels of SES, and because reducing SES inequities is a major goal for many policy makers and firms. Firms and policy makers serve individuals with varying levels of SES; our investigation can help them estimate which consumers their nudges will impact most. Furthermore, SES has robust positive associations with numeracy, domain knowledge, and anxiety ([ 3]; [48]; [71]), which, in our view, shape susceptibility to nudges.We examined numeracy and domain knowledge as focal moderators because these constructs play major roles in consumer decision making ([28]; [53]) and are useful for theory building. As we explain in the following section, these variables, along with anxiety, decision uncertainty, and preference construction, determine the extent to which choice architecture influences decisions according to our account.Understanding heterogeneous effects of nudges could help firms by allowing them to target specific consumer segments, which could make nudges more effective. Furthermore, scholars have suggested that understanding heterogeneity would provide insight into why nudges often have smaller effects when applied at scale ([74]). Theoretical BackgroundSocioeconomic disparities pervade consumer behavior. SES influences what products and brands consumers buy, how they access credit, and how they are treated in some stores, among other impacts ([13]; [23]; [34]). Consumers with lower SES and education (as well as the elderly) are often more vulnerable to marketing scams and manipulations ([34]; [43]). In addition, there are wide gaps between high-SES and low-SES consumers in terms of how much money they have in stocks, retirement savings, credit card debt, payday loan debt, and other assets or liabilities, which can greatly influence present behavior and future wealth ([ 8]; [23]).Lower SES is associated with lower levels of numeracy and financial literacy ([ 3]; [48]; [71]). These skills play a role in nearly every type of consumer decision (e.g., [28]; [71]), and the discrepancy in these skills between low-SES and high-SES consumers can lead to disparate decisions and outcomes. The experience of scarcity that accompanies low SES sometimes narrows attention on a focal decision and influences time allocation, which can impact decisions ([69]).Numeracy is the ability to process and use basic numerical concepts; make quantitative estimations; and use probabilities, percentages, and ratios ([58]). In the context of consumer decision making, innumerate people cannot calculate unit prices, use percentages to calculate discounts, compute interest, or even estimate a tip accurately ([28]; [53]; [63]). Broadly, numerate individuals often make better consumer and health decisions, especially when these decisions involve numbers, calculations, prices, or financial information ([59]). Numeracy refers to the ability to use and process numbers, which is distinct from other traits such as self-efficacy, math emotions (e.g., math anxiety), uncertainty, and subjective numeracy ([57]; [58]; [71]).Financial literacy is the knowledge of basic financial concepts, operations, and facts. It is an important skill used to make financial decisions as well as decisions involving product prices and attributes ([19]; [32]). Financially literate consumers are less likely to overspend and are more likely to save for retirement, invest in stocks, comparison shop, and pay off their full credit card balance ([19]; [32]; [47]). Though financial literacy is associated with a wide range of consumer behaviors, financial literacy training only weakly influences financial knowledge, and any effects dissipate quickly ([25]).Numeracy and financial literacy impact consumer behavior partly because consumers with lower numeracy and financial literacy experience greater anxiety and decision uncertainty when dealing with numbers and financial decisions ([58]; [71]). In addition, rather than retrieve stable preferences from memory, uncertain consumers construct their preferences on the fly more often than do consumers with higher certainty ([35]). As a result, their preferences are more labile; they are more reliant on effort-reducing heuristics; and they are more impacted by defaults, the status quo, and changes in the number of options ([16]; [36]; [37]; [68]). In other words, consumers with lower numeracy and financial literacy feel more uncertainty and anxiety; thus, we expected that in the context of nudges they would rely on strategies such as choosing the default option or first option presented. Furthermore, we hypothesized that low-SES consumers would be more impacted by nudges because they score lower in relevant skills such as numeracy and because they experience more anxiety when making decisions (Figure 1).Although the constructs of decision uncertainty, preference construction, anxiety, subjective knowledge, and the three focal moderators (SES, numeracy, and domain knowledge) are all associated with one another, they are distinct ([57]; [58]; [71]). Furthermore, they differ from general intelligence and other types of confidence (e.g., general self-efficacy vs. search confidence; [25]; [55]). These constructs have clear discriminant validity; for example, objective numeracy and domain knowledge are types of objective knowledge or skill, which differ from subjective beliefs about ability (e.g., subjective numeracy, confidence, uncertainty; [29]; [58]). Many people have high confidence in their numeric abilities despite low objective numeracy or vice versa, either of which can lead to harmful financial and health outcomes ([58]). In addition, subjective confidence and anxiety differentially predict memory and evaluations ([57]). Decision anxiety can also impact performance independent of objective numeracy. For example, people can be anxious about disconfirming negative stereotypes despite high objective ability. This can create a self-fulfilling prophecy, because the feeling of stigmatization can increase anxiety and negatively impact decisions ([76]).Although previous choice architecture research has typically focused on the average effects on consumers, some individual difference moderators of choice architecture effects have been identified. However, nearly all of these moderators have been tested for only a single type of nudge within a single domain. Next, we summarize the research about choice architecture moderators that is most relevant to our hypotheses.Very little previous research has examined whether the impact of nudges is moderated by SES. For other marketing manipulations such as scams, previous research has suggested that vulnerable consumers (e.g., those who are elderly or less educated) are sometimes targeted and impacted to a greater extent ([33]; [34]; [43]). Within the context of nudges, recent unpublished papers have found that automatic retirement contributions increased savings more for younger and lower-income individuals than others ([ 9]; [18]). This conflicts with other scholars who have made theoretical claims that low-SES individuals are less nudgeable ([61]). Other work has provided mixed evidence about whether low- or high-income individuals are more impacted by different nudges such as framing ([26]; [31]; [69]). Clearly, more research is needed to test these opposing claims across a wide variety of nudges and contexts.Some theorists have previously suggested that people with more expertise or knowledge might be less impacted by choice architecture. For example, [12] claimed that the aim of policy nudges is to create large benefits for those who have lower expertise and make errors, with minimal impact on more rational or expert decision makers. In other words, consumers with more knowledge or expertise may be less impacted by nudges. However, this claim has received very little empirical attention. One investigation found no default effect in the environmental domain among a sample of environmental economists ([46]). However, the study did not measure experience, examine moderators, or include a control group of people with low experience, so it is difficult to draw conclusions from it. Another investigation found no effect of experience or education on default effects ([38]). There has been some relevant previous research on numeracy. [59] found that numerate people are less impacted than innumerate people by manipulations that present numbers as frequencies rather than probabilities, while [14] found the opposite in the context of Bayesian reasoning. Prior research has not examined whether financial literacy and numeracy moderate effects of defaults, sorting, or other choice architecture tools. Clearly, the present studies are needed to clarify these relationships. Overview of StudiesAcross six studies, we tested whether nudges have larger impacts on low-SES consumers and those with lower numeracy and domain knowledge. In Study 1, we demonstrate these effects in the context of consumer financial decisions such as selecting which credit card to acquire. In Study 2, we show that the findings of Study 1 generalize across different consumer decision contexts (consumer sustainability decisions, consumer financial decisions, and retail product choices) and different types of choice architecture (interventions that sort options, preselect a default, and reduce the number of options, specifically). In Study 3, we used data from individuals whose employers by default automatically enrolled them into a retirement plan, testing whether consumers with lower SES and domain knowledge were more likely to accept the default enrollment according to self-reported decisions in this high-stakes real-life context. In Study 4, we examined whether the effects of domain knowledge and SES generalize to a vastly different domain: consumer health decisions in the context of COVID-19. Finally, in Study 5 and a supplemental study in the Web Appendix, we conceptually replicated Study 1 while addressing alternative explanations and examining proposed mediators.We preregistered all studies at aspredicted.org, except Study 3, which used an existing data set. To eliminate the file drawer problem for this research, we report all studies that we conducted and all preregistered analyses for each study. Data, preregistrations, and analysis scripts are available at https://osf.io/a7b32/?view%5fonly=f4df788f178844f6b26e5274a9cbdab1, with the exception of Study 3, which was from a syndicated panel that we do not have permission to share. Across studies, we sought converging evidence for our hypotheses. Study 1: Do Defaults Reduce Disparities?In Study 1, participants made five consumer financial decisions. For each decision, they were randomly assigned to a good-default, bad-default, or no-default condition. We hypothesized that good defaults would benefit consumers with low SES, low financial literacy, and low numerical ability more than consumers with high SES, financial literacy, and numerical ability. The Study 1 hypotheses, sample size, and analysis plan are available at https://aspredicted.org/blind.php?x=x547ih. Method ParticipantsWe requested 450 participants from ROIRocket. Participants (53.1% female; Mage = 50.2 years) were given $1 upon completion of the study. ROIRocket provides a population inexperienced with academic surveys (median of two previous academic surveys; see the Web Appendix), and substantially less experienced than participants on MTurk. To increase statistical power to attain SES effects and ensure that we had enough SES variability, we requested that ROIRocket oversample people who did not finish high school as well as people with advanced degrees (in Study 1 only). ROIRocket provided us with far more participants than requested (N = 825). We included in primary analyses all 825 participants who finished the study.[ 7] ProcedureAfter the consent process, participants made five focal decisions. These decisions are displayed in Table 1. For example, one decision asked participants whether they would repay interest on a high-interest credit card or lower-interest card if they had equal debt on both cards (a common task similar to [ 5]]). Participants were asked to select the option that had the largest total monetary benefits minus costs. These five questions each had a mathematically correct option that would save the most money if it were a real-life decision.GraphTable 1. Questions and Answer Options Used in Study 1. 40022242921993180 a The questions presented in this table are abbreviated; for exact text, see the Web Appendix.50022242921993180 b The options presented in italics are the correct answers.60022242921993180 Notes: The percentages listed are the percentages who chose the correct answer. Overall, across each item, accuracy was significantly higher in the no-default condition compared with the bad-default condition and significantly higher in the good-default condition compared with the no-default condition. APR = annual percentage rate.For each question, participants were randomly assigned to one of three default conditions. In the no-default condition, no answer was preselected. In the good-default condition, the correct option (which would save the consumer the most money) was preselected. In the bad-default condition, an incorrect (i.e., more costly) option was preselected. Participants in the good- and bad-default conditions were told, ""An option has been pre-selected for you. You may keep that selection or switch to another option."" Because the default condition was randomly determined for each question, participants received different conditions for different questions. We used this design to increase power ([52]).After making the five focal decisions, participants completed measures designed to assess their predictions about how much they were influenced by the defaults. Two questions asked them how likely they thought they would be to get a focal consumer financial decision correct if ( 1) the correct answer was preselected or ( 2) if an incorrect answer was preselected.Then, participants completed measures of the factors we predicted would moderate nudge effects—financial literacy, numeracy, and SES. They also completed exploratory measures of agreeableness, need for cognition, self-reported credit score, and self-reported patience (for text of all measures, see the Web Appendix). To assess financial literacy, we used a common scale ([25]) that asked participants multiple choice questions about common financial instruments and techniques such as stocks, 401(k)s, and diversification (α =.85). We measured numeracy with 11 questions (α =.87) that assessed understanding of probability, frequency, and percentages ([44]). Following previous research and American Psychological Association recommendations for measuring and conceptualizing SES ([ 1]; [62]), the SES measure included three components: education level, occupation status, and income. As in previous SES research, we standardized and averaged the three components for analysis ([ 1]). The measure had high internal consistency (α =.78). Factor analyses indicated that the SES, financial literacy, and numeracy items loaded on three separate factors as expected (Web Appendix Tables A1 and A2; oblimin rotation was used).We included measures of agreeableness and need for cognition in this study to address alternative explanations that agreeable personalities or desires for elaborative thought (rather than SES and domain knowledge) might explain differences in default effects across people. We also measured the total time participants spent completing the study (log-transformed as preregistered), which served as a proxy for overall survey engagement.In addition, we included assumption check items to ensure that the correct answers were best for a wide variety of people, including those with low SES and few liquid assets (details in the Web Appendix). After responding to the main measures in Study 1 but before reporting demographics, participants made three consumer decisions with no correct answer, so that we could ensure that the focal moderators generalize beyond the context of questions with a correct answer. The three items asked participants to choose which flight insurance option to buy; which laptop computer to buy based on price, image, and consumer reviews; and which painkiller to purchase based on price and brand. Each item had three options and participants saw each item with one of the three options preselected or with no option preselected. Results from these questions were not included in the primary analyses mentioned in the preregistration, so we label these analyses as exploratory and report them separately from primary analyses.Participants also reported demographics and how many past studies they had completed. We included an attention check to ensure that effects were robust when accounting for people who rushed through the survey. The attention check asked them to select a particular answer for a fake question added in the middle of the financial literacy scale. Following our preregistration, we included all participants, including those who failed the attention check, in primary analyses (though all effects remained significant when excluding attention check failures). Analytical approachIn each study, we analyzed results using binomial generalized mixed effects models. We estimated decision accuracy (1 = correct, 0 = incorrect) as the dependent variable and treated participants as random factors to properly model variance across people ([ 6]). As preregistered, the models in studies with three default conditions included a contrast-coded default condition term (1 = good default, 0 = no default, −1 = bad default) and the orthogonal contrast. All models contained item fixed effects that accounted for variation in difficulty across different questions. We tested hypothesized moderators of default effects and standardized the moderating variables. ResultsDefaults strongly influenced decisions on average. Participants in the bad-default condition answered 62% of items correctly (choosing the most advantageous option) compared with 71% in the no-default condition and 78% in the good-default condition (z = 10.73, Exp(B) = 1.62, p <.001). Simple effects tests indicated that the difference between the no-default and good-default conditions was sizable (z = 4.39, Exp(B) = 1.62, p <.001), as was the difference between the no-default and bad-default conditions (z = −4.63, Exp(B) =.61, p <.001). Socioeconomic statusAs predicted, there was an SES × default condition interaction, such that default effects were larger among lower-SES consumers than higher-SES consumers (z = −3.64, Exp(B) =.83, p <.001; Figure 2, Panel A). Simple effects tests indicated that default effects were over 2.2 times larger for people in the bottom half of the SES distribution compared with the top half. SES was weakly correlated with survey engagement (r =.03), and its interaction with default condition was robust when controlling for engagement (z = −3.65, p <.001).Graph: Figure 2. Default effects were larger among consumers lower in SES, lower in financial literacy, and lower in numeracy.Notes: The bad-default, no-default, and good-default conditions are depicted by dashed, dotted, and solid lines, respectively. Good nudges reduced disparities, as depicted by the small difference between consumers low and high in each variable in the good-default condition (shallow solid line) compared with the no-default and bad-default conditions (steeper dotted and dashed lines). Shaded regions depict ±1 SE. The histograms along the x-axis depict the distribution of each moderator. Financial literacyAs we predicted, there was a large financial literacy × default condition interaction (z = −6.32, Exp(B) =.75, p <.001; Figure 2, Panel B). Participants lower in financial literacy were impacted by defaults more than participants higher in financial literacy. This interaction remained significant when controlling for SES, numeracy, and their interactions with default condition (z = −2.41, Exp(B) =.86, p =.016). NumeracyThere was also a numeracy × default condition interaction (z = −6.83, Exp(B) =.74, p <.001; Figure 2, Panel C), such that those with lower numerical ability were impacted by defaults more than those with higher numerical ability. This interaction remained significant when controlling for SES, financial literacy, and their interactions with default condition (z = −3.63, Exp(B) =.82, p <.001). This implies that the interactions with numeracy and financial literacy were at least partly independent effects.Our conceptual framework (Figure 1) suggests that financial literacy and numeracy account for the SES × default condition interaction. Consistent with this, the SES × default condition interaction was greatly reduced when we controlled for numeracy, financial literacy, and their interactions with default condition (z = −.46, Exp(B) =.97, p =.648). In the Web Appendix, we show that mediation models were also consistent with this idea that numeracy and financial literacy account for the moderating effects of SES on default effects. Questions with no correct answerWe also conducted exploratory analyses of three consumer choice questions with no correct answer. We included these items to examine whether the key results (that consumers lower in SES, numeracy, and domain knowledge are more impacted by defaults) generalized beyond the context of questions with a correct answer. Participants with lower SES were more likely to retain default options on average (z = −3.22, Exp(B) =.81, p =.001). In addition, those with lower financial literacy were more likely to retain the default options (z = −4.36, Exp(B) =.79, p <.001), as were those with lower numeracy (z = −4.85, Exp(B) =.76, p <.001). This suggests that participants with low SES, low financial literacy, and low numeracy are more likely to choose default options and that our key findings are not simply the result of participants with low SES, low financial literacy, and low numeracy having less access to correct answers. Mispredicting default effectsParticipants predicted that defaults would have little, if any, impact on their decision accuracy. We asked participants two questions in which they reported how likely they thought it was that they would answer a focal consumer financial decision question correctly ( 1) if the correct answer was preselected and ( 2) if an incorrect answer was preselected (in each case, they were asked to assume they were not told whether the default option was correct).Participants thought their likelihood of answering correctly would be 65% if assigned to a good default and 64% if assigned to a bad default (t(824) = 1.84, p =.066). Financial literacy, numeracy, and SES were not significantly associated with participants' predictions of how much they would be impacted by defaults (see the Web Appendix; if anything, more numerate consumers thought they would be impacted more by defaults, though they were actually less impacted). Interestingly, participants were not overconfident on average; they were simply miscalibrated about default effects. They greatly underestimated how accurate they would be when assigned to a good default (estimates = 65%, reality = 78%) and were close to reality when regarding bad defaults (estimates = 64%, reality = 62%). Robustness testsWe preregistered the following three robustness tests. In the first, we wanted to control for how engaged participants were with the study (assessed via the log-transformed time they spent completing it). In the second, we controlled for agreeableness and need for cognition.[ 8] In the third, we excluded participants who failed the attention check. All three focal moderators remained significant and similar in size across all of these robustness tests (all zs < −3, all ps <.001; for further details, see the Web Appendix). DiscussionAs we predicted, consumers who had lower SES, lower financial literacy, and lower numeracy were more impacted by defaults than consumers who had higher SES, higher financial literacy, and higher numeracy. In other words, good defaults were an equalizer that helped reduce the differences in decision quality between consumers with low versus high SES, numeracy, and financial literacy. Interestingly, participants seemed largely unaware of the impact of defaults. They did not anticipate that defaults would influence their behavior, nor did consumers lower in SES, financial literacy, or numeracy predict they would be impacted more. It is worth noting that we oversampled people with very low or very high education in Study 1. Although this increased statistical power, the sample was different from the general population. In subsequent studies, we use more balanced samples (with no oversamples), and in Study 3 we use a more representative stratified random sample of U.S. households.In a supplemental study, we addressed alternative explanations for the effects found in Study 1, namely that effects of financial literacy and numeracy might be explained by participants who were not understanding the questions, not paying attention, or not conscientious (see Web Appendix). In this supplemental study, we replicated these key interactions from Study 1 and showed that these were robust even when controlling for comprehension of the decision questions and individual differences in conscientiousness. This suggests that people lower in numeracy and domain knowledge are impacted more by defaults, that these effects are replicable, and that they are not attributable to low conscientiousness or poor comprehension.Study 1 highlights how default effects are moderated by differences in financial literacy, numeracy, and SES. In Study 2, we wanted to examine whether these results generalize across three different types of nudges in three decision-making contexts with incentives for accuracy. Study 2: Do Nudges Reduce Disparities Across Different Contexts and Types of Nudges?Study 2 was designed primarily to test whether the results observed in Study 1 generalize across different types of nudges and across different consumer contexts. In addition, we added incentives for half of the decisions to examine whether incentives moderate the effects observed in Study 1. We expected that moderators observed in Study 1 would generalize across the three nudge types, across the three contexts, and across incentivized and nonincentivized decisions. We also included a measure of general fluid intelligence to isolate domain knowledge from general intelligence. We preregistered sample size, predictions, and analyses at https://aspredicted.org/blind.php?x=v3ci5q and report all preregistered analyses. Method ParticipantsROIRocket respondents (N = 428; 51.6% female; Mage = 53.2 years) participated in exchange for a fixed payment of $1 and a $2 bonus if they answered one of the focal consumer financial decisions correctly. In this and all subsequent studies, participants who had completed any of our previous studies were not allowed to participate.[ 9] ProcedureThe procedure was similar to Study 1, but with three different types of nudges and with decisions that spanned three different contexts. Participants answered six focal questions with mathematically correct answers. The three contexts were retail product choices, consumer financial decisions, and consumer sustainability decisions. The two retail product choices involved choosing a computer with or without insurance, and food with the lowest price per ounce. The consumer financial decisions were slightly altered versions of the debt repayment and retirement questions used in Study 1. The consumer sustainability decisions involved choosing window insulation that would maximize total savings and choosing lightbulbs with the lowest unit price. Participants were asked to choose the item with the lowest average monetary costs and were incentivized to choose these options for half the questions. The Web Appendix provides the full text of each question. The three types of nudges were defaults, sorting, and number of options. The default manipulation was similar to Study 1 but with only the good-nudge and no-nudge conditions (because these often have higher ecological validity),[10] the sorting manipulation varied whether options were ordered from best to worst (""good sort"") or randomly (""no sort""), and the number of options manipulation varied whether ten options were presented (""many options"") or only two of the best options (""few options""), following [67]. All sorting and default questions had ten options.The design was thus a 3 (context: retail product choices, consumer financial decisions, consumer sustainability decisions) × 3 (nudge type: defaults, sorting, number of options) × 2 (nudge condition: good nudge, no nudge) × 2 (incentive: $2, $0) experimental design. The questions were organized in three blocks in counterbalanced order corresponding to different contexts and nudge types.[11] The first three questions were incentivized for some participants and the last three questions were incentivized for others.Following the six focal decisions, participants completed the same measures of financial literacy as in Study 1 and a shortened three-item version of the numeracy measure ([65]) to reduce the length of the survey. To isolate domain knowledge effects (financial literacy) from general intelligence, we included a measure of general fluid intelligence called number series ([51]). The measure asked participants to answer six questions that involved completing a pattern of numbers such as ""23, 26, 30, 35, __"" (correct answer: 41). Then, participants completed the three-item of measure of SES described in Study 1. Finally, participants reported their credit score range, completed the attention check item, completed a measure of time preferences (see the Web Appendix), and reported their age and gender. ResultsOn average, the nudges had their intended effects. We estimated accuracy in binomial mixed-effects models as a function of nudge condition (contrast-coded), with the rest of the model the same as in Study 1. Accuracy was higher when good nudges were used (M = 56%) compared with no nudge (M = 42%; z = 7.49, Exp(B) = 1.87, p <.001). These effects were strong for the default and number of options nudges but nonsignificant for sorting (Mgood default = 55%, Mno default = 40%; Mfew options = 68%, Mmany options = 43%; Mgood sort = 46%, Mno sort = 43%). Socioeconomic statusNudge effects were moderated by SES such that they impacted low-SES participants more than high-SES participants (z = −2.92, Exp(B) =.77, p =.004). That is, nudges designed to facilitate selection of the best option reduced choice disparities by helping low-SES consumers more than high-SES consumers. Consistent with our framework (Figure 1), when we included financial literacy and the financial literacy × nudge condition interaction in the model, the SES × nudge condition interaction was no longer significant (χ2( 2, n = 413) = 1.87, p =.170). The SES × nudge condition interaction was not significantly moderated by nudge type (χ2( 2, n = 428) = 2.74, p =.255) or decision context (χ2( 2, n = 428) = 4.09, p =.129). It was also robust when controlling for survey engagement (z = −2.91, p =.004), and SES was very weakly correlated with survey engagement (r =.02). Financial literacyAs we predicted, nudges had more impact on consumers with lower financial literacy than those with higher financial literacy (z = −2.42, Exp(B) =.80, p =.015). Figure 3 shows the robust effects of financial literacy across studies. These financial literacy × nudge condition interactions were not significantly moderated by the type of nudge (χ2( 2, n = 428) =.47, p =.790) or by the decision context (χ2( 2, n = 428) = 3.06, p =.216).Graph: Figure 3. Forest plot conveying the three moderators of nudge effects across studies.Notesz: The effects were relatively consistent and robust across studies, though numeracy and SES had nonsignificant effects in one study each. Study 3 is omitted because it used a different dependent variable (self-reports of whether participants retained default retirement options). NumeracyUnlike in Study 1 and all of our subsequent studies, numeracy did not moderate the impact of nudges (z = −.27, Exp(B) =.97, p =.785). In the Web Appendix, we explore different reasons for this difference, concluding that this is partly attributable to low reliability and lower validity on the three-item numeracy scale in Study 2 (α =.53) compared with the longer and more sensitive numeracy measure used in Study 1 and subsequent studies (Study 1: α =.87). The relationship between numeracy and nudge effects was not moderated by the type of nudge (χ2( 2, n = 428) =.57, p =.751) or by the decision context (χ2( 2, n = 428) = 1.57, p =.457). General fluid intelligenceWe predicted that general intelligence would also moderate default effects but that it would not fully account for the financial literacy effect. Contrary to our expectations, consumers who scored higher on the measure of general fluid intelligence were not significantly less susceptible to nudges (z = −1.41, Exp(B) =.87, 95% confidence interval [CI] = [.72, 1.05], p =.157). The financial literacy × nudge condition and SES × nudge condition interactions remained significant when controlling for fluid intelligence (both zs = −2, ps <.05). This finding suggests that financial literacy and other forms of domain-specific knowledge likely influence nudge effects more than general fluid intelligence. Robustness testsWe controlled for survey engagement, which did not appreciably change the interactions of condition with financial literacy or SES (both zs < −2, both ps <.05). IncentivesThe incentive manipulation did not significantly influence accuracy (Mincentivized = 51%, Mnonincentivized = 48%; z = 1.35, Exp(B) = 1.13, p =.178), though it did increase the amount of time participants spent on the questions (Mincentivized = 126 seconds, Mnonincentivized = 87 seconds; t(2,132.01) = 4.23, b =.11, p <.001). On average, the nudges increased accuracy about four times more than a $2 incentive. The key interactions were not any smaller for the incentivized questions than the nonincentivized questions (see the Web Appendix). DiscussionConsistent with Study 1, SES and financial literacy each moderated the effects of nudges in Study 2. These effects were present even though decisions were incentivized. It is not surprising that the effect of financial literacy was not moderated by the decision context, because the decisions we examined in Study 2 all involved numbers, prices, financial information, or calculations. As mentioned previously, financial literacy and numeracy are useful across many contexts of consumer choice because they are used to compare prices and quantities, calculate unit prices, and calculate cost effectiveness and long-term savings ([28]; [63]). Thus, we did not expect context to moderate effects of financial literacy in Study 2.Although the results of Studies 1 and 2 demonstrate important and consistent effects, it is not yet clear whether the results generalize to high-stakes, real-life decisions. Therefore, in Study 3, we use data about Americans' self-reported retirement investment choices. We examine whether defaults influence low-SES consumers more than high-SES consumers in this context. Study 3: Defaults and Retirement Decision DataIn Study 3, we acquired (self-reported) data about Americans' retirement investment decisions. We examined a sample of consumers who work for companies that set defaults by automatically enrolling employees into retirement contributions. Respondents were asked whether they opted out of the default contribution amount and default investment allocation set by their company. We predicted that consumers lower in SES and financial knowledge would be more likely to choose the default options than those with higher SES and financial knowledge. MethodThe secondary data we used consisted of stratified random samples of U.S. households. The panel, Strategic Business Insights (SBI) MacroMonitor, is a syndicated panel that asks respondents questions about their financial decisions and demographics. The panel is conducted with different households every other year. We were given access to four different samples from the panels that were conducted in 2010, 2012, 2014, and 2016, respectively.Our primary interest was in three questions that asked respondents whether they accepted or rejected their employer's default options in real retirement decisions. Specifically, respondents were asked whether their current employer automatically enrolled them into a retirement plan (753 indicated yes, 3,580 indicated no, and the rest selected ""does not apply"" because they were retired or unemployed). Following this, respondents who had answered ""yes"" were asked two questions assessing ( 1) whether they kept the default contribution percentage and ( 2) whether they kept the default investment allocation. Of those who reported they were automatically enrolled, 48% indicated they accepted the default investment allocation, whereas 52% opted out and chose a different allocation. For the default contribution amount question, 45% indicated they had accepted the default contribution amount, whereas 55% opted out. We analyzed default selection (1 = chose default option, 0 = opted out of default) in binomial generalized mixed models as a function of the question (allocation or amount) and hypothesized predictors.We examined measures of SES and financial sophistication. The SES measure followed the preregistered measure used in Study 2 as closely as possible (education, income, and occupation, standardized and combined; for details, see the Web Appendix).Self-reported financial sophistication was analyzed using the following two measures, consistent with previous research that used the SBI MacroMonitor data ([54]). The self-reported financial sophistication item asked participants to rate their agreement with the statement ""I consider myself a sophisticated investor"" (1 = ""mostly disagree,"" and 4 = ""mostly agree""). The financial experience item asked respondents whether they handle their household's financial investments. Other items were assessed in the survey, including gender, age, marital status, number of children, U.S. census region, religion, race, hours worked per week, and risk aversion. Results Socioeconomic statusWe first tested whether low-SES individuals were more likely to choose the default options. Participants with lower SES were more likely to accept the default options (z = −5.71, Exp(B) =.33, p <.001). Financial sophistication and experienceWe computed a model estimating default choices as a function of self-reported financial sophistication and financial experience. Individuals with lower financial sophistication were more likely to accept the default option (z = −5.62, Exp(B) =.40, p <.001), as were those with lower financial experience (z = −2.88, Exp(B) =.66, p =.004). These effects are broadly consistent with Studies 1 and 2, though SBI used measures of financial sophistication that differed from the financial literacy scale we used in the experiments we designed. Robustness testsWe also conducted a robustness test in which we controlled for all the covariates listed in the ""Method"" subsection. This was designed to address alternative explanations that the effects of SES and financial sophistication were actually explained by differences in any of these other variables. When adjusting for these covariates, the effects of SES, financial sophistication, and investment experience remained significant (all zs < −3, ps <.01). SES and financial sophistication influenced both default questions individually (Web Appendix). DiscussionThe results of Study 3 demonstrate that consumers with low SES and low financial sophistication are more likely to retain default options, even in self-reports of their high-stakes retirement decisions. This is consistent with working papers that found larger effects of automatic enrollment for younger and low-income individuals compared with older and high-income individuals ([ 9]; [18]). It is worth noting that typical default enrollment rates of 3% and 6% are likely insufficient for many people, and it is possible that some respondents who opted out chose higher amounts in Study 3. Therefore, we cannot infer that automatic enrollment improved decisions.Though Studies 1–3 suggest the results generalize to many important decisions, most of the decisions we examined were consumer decisions with prices or financial elements. In Study 4, we demonstrate generalizability further by examining a dramatically different context of health decisions in the early stages of the COVID-19 pandemic. Study 4: Do Defaults Reduce Disparities in COVID-19 Consumer Health Decisions?In Study 4, we aimed to generalize our results from Studies 1–3 to questions about optimal behavior during the COVID-19 pandemic. We hypothesized that participants with lower SES, numeracy, and health literacy would be impacted more by defaults in this context. We also tested whether domain-specific health knowledge moderated nudge effects more than less relevant financial knowledge. Thus, unlike in the previous studies, we did not predict financial literacy would moderate default effects, because it is less relevant for health decisions. Instead, we predicted that health literacy would moderate default effects. We preregistered the sample size, hypotheses, and analyses at https://aspredicted.org/blind.php?x=an4kx6. Method ParticipantsParticipants from ROIRocket completed the experiment in exchange for $.50 (N = 305; 50.8% female; Mage = 52.0 years). This experiment was conducted in April 2020 while much of the United States was under restrictions designed to slow the spread of COVID-19. ProcedureParticipants answered four questions about how they would respond to different scenarios in the context of COVID-19. The four questions, respectively, asked participants whether they would wear a mask in public, how they would disinfect surfaces, what they should do if they have an upset stomach and runny nose, and how long to wait before touching packages delivered to the door (for full text, see the Web Appendix). Participants were told to follow Centers for Disease Control and Prevention guidelines and assume that those guidelines were all correct. Answers were coded for accuracy (1 = correct, 0 = incorrect). For each question, participants were assigned to either the no-default or good-default condition. We did not include a bad-default condition, because it could spread misinformation about COVID-19. The questions assigned to each condition were counterbalanced, and participants received two questions in each condition.Following these four questions, participants completed measures of numeracy, financial literacy, health literacy, SES, other demographics, and an attention check. We used a longer nine-item numeracy measure in Study 4 ([44]), because we suspected that the null numeracy result in Study 2 was due to low reliability of the three-item measure. The numeracy measure included two subscales consisting of health numeracy questions (six items) and general numeracy questions about lotteries (three items), respectively. The health literacy measure included items such as interpreting ""drug facts"" from a medicine label (see Web Appendix). The other measures (financial literacy, SES, and attention check) were the same as in Study 2. ResultsOn average, good defaults increased accuracy compared with the no-default condition. Accuracy was significantly lower in the no-default condition (M = 64%) compared with the good-default condition (M = 72%; z = 3.35, Exp(B) = 1.58, p <.001). Socioeconomic statusAs we predicted, consumers with lower SES were more impacted by defaults, as indicated by the SES × default condition interaction (z = −2.31, Exp(B) =.73, p =.021). The default effect was over four times larger among consumers with below-average SES compared with those with above-average SES. This effect was no longer significant when we added numeracy to the model (z = −1.43, Exp(B) =.81, p =.151), consistent with Figure 1. Health numeracy and general numeracyOverall, numeracy moderated default effects: less numerate participants were more impacted by defaults than numerate participants (z = −2.57 Exp(B) =.71, p =.010). To examine whether domain-specific health numeracy impacted decisions more than general numeracy, we also separately examined subscales that assessed health numeracy and general numeracy, respectively. Health numeracy significantly moderated the default effects, such that those with lower health numeracy exhibited larger default effects (z = −2.83, Exp(B) =.79, p =.004). In contrast, the general numeracy subscale did not significantly moderate default effects (z = −1.57, Exp(B) =.81, p =.117). Health literacy and financial literacyWe predicted that same-domain (health) knowledge would influence default effects more than other-domain knowledge (e.g., financial literacy). Consistent with this, financial literacy did not significantly moderate the default effects (z =.55, Exp(B) = 1.02, 95% CI = [.83, 1.40], p =.582). Note that one cannot conclude from a nonsignificant result that the moderating effect of financial literacy is zero. However, the 95% CI includes only small positive or negative effects that are smaller than the moderating effects of numeracy and SES (for Bayes factor analyses, see the Web Appendix).Although we expected health literacy to significantly moderate default effects, this result was only marginal (z = −1.81, Exp(B) =.79, 95% CI = [.60, 1.02], p =.070). As detailed in the Web Appendix, we suspect that this health literacy result was marginal and smaller than expected because nearly all participants scored very high on the measure (giving us low power due to the low variability). Health literacy was weakly correlated with SES (r =.13). Robustness testsWe preregistered two robustness tests that excluded attention check failures and adjusted for survey engagement, respectively. All significant interactions with default condition remained significant in these robustness tests (all zs < −2, ps <.05). Mediation modelWe used a bootstrapped mediation model with 5,000 resamples ([60]) to examine whether consumers with low SES are more nudgeable because they are less numerate (see Figure 1). There was a significant indirect effect consistent with the proposed path from lower SES to lower numeracy to larger default effects (indirect effect = −.07, 95% CI = [–.13, −.02]). The effect of SES on the size of default effects was reduced when numeracy was added to the model (from c = −.19, 95% CI = [–.31, −.06] to c1 = −.11, 95% CI = [−.25,.03]), consistent with our predictions. An alternative mediation possibility is that SES influences nudges by causing consumers to allocate time differently ([69]). Contrary to this possibility, SES was not associated with time spent on these questions (z = −.19, Exp(B) =.99, p =.847), and there was no indirect effect of SES on default effects through decision time in a parallel mediation model (ab =.00, 95% CI = [−.01,.01]). DiscussionThe results of Study 4 replicate and extend the results of previous studies to the context of COVID-19 health decisions. Low-SES people benefited disproportionately from nudges even in the context of questions about COVID-19. Low-SES people are disproportionately affected by COVID-19 and thus have the most to gain from interventions that help them. Mediation models were consistent with our framework in which low-SES individuals are more impacted by nudges, not because they allocate time differently but because of differences in domain-specific skills. In Study 5, we test the remainder of our conceptual diagram in sequential mediation models. Study 5: Why Do Numeracy and Financial Literacy Moderate Nudges?Study 5 had two purposes. First, we generalized our results across two different samples, including a sample of Master of Business Administration (MBA) students at an elite university. This would ensure that our findings generalized beyond a sample with relatively low financial knowledge. Second, we tested the proposed mediation model displayed in Figure 1 about why financial literacy, numeracy, and SES moderate default effects. Consumers with low numeracy and financial literacy experience greater uncertainty and anxiety when facing consumer decisions involving numbers or math ([71]). In turn, anxiety and uncertainty likely increases susceptibility to default effects (e.g., [36]). We tested these proposed paths with mediation models. In addition, we hypothesized that financial literacy, numeracy, and SES would moderate default effects, replicating the results of our previous studies. We preregistered the sample size, hypotheses, and analyses at https://aspredicted.org/blind.php?x=4yz385. Method ParticipantsWe requested and preregistered a sample of 200 participants from ROIRocket and an estimated 100 MBA students. All participants received a $2 bonus if they answered one randomly selected focal financial question correctly. ROIRocket participants also received $1 fixed payment, whereas MBA students received points for a minor class assignment. The ROIRocket sample was more diverse and older (n = 212; 50.9% male; median age = 54 years) than the MBA sample (n = 75; 61.3% male; median age = 29 years). The MBA students had higher financial literacy and numeracy compared with ROIRocket participants (financial literacy questions answered correctly: MMBA = 87%, MROIRocket = 64%; numeracy questions answered correctly: MMBA = 90%, MROIRocket = 53%; both ts > 3, ps <.001). ProcedureThe procedure was similar to Study 1, except for the following differences. All five decisions were incentive compatible, and one retail product choice (of laptops with different insurance options) was added (also used in Study 2).[12] We also examined potential mediators by assessing perceived uncertainty, decision anxiety, and preference construction; we suspected that each of these three variables partially accounts for the effects of domain-specific skills on default effects (as described previously). The three factors, though correlated, had discriminant validity (see the Web Appendix) and have also been differentiated in previous research ([57]; [58]; [71]). ResultsWe used the same model structure as in Study 1. Participants were more likely to choose the correct answer in the good-default condition (M = 63%) than in the no-default (M = 60%) and bad-default conditions (M = 56%; z = 3.23, Exp(B) = 1.33, p =.001). Socioeconomic statusUnlike in the previous studies, SES did not significantly moderate the effects in Study 5 (z =.12, Exp(B) = 1.01, p =.901).[13] SES was not correlated with survey engagement either (r =.00). Financial literacyAs we predicted, consumers with lower financial literacy were more impacted by defaults as in the previous studies (z = −4.21, Exp(B) =.70, p <.001; Figure 3). The default effect was over five times larger among consumers with below-average financial literacy compared with those above-average financial literacy. When we controlled for the numeracy × default condition interaction, the financial literacy interaction remained significant. NumeracyParticipants low in numeracy were also more impacted by defaults as indicated by the numeracy × default condition interaction (z = −2.81, Exp(B) =.78, p =.005). Robustness testsFinancial literacy and numeracy moderated the default effects even when adjusting for survey engagement (and when adjusting for MBA vs. ROIRocket participants; all zs < −2.5, ps <.01). When we excluded attention check failures, the interaction with financial literacy remained similar in size, though the interaction with numeracy reduced slightly and was marginal (financial literacy: z < −2, p <.01; numeracy: z = −1.84, Exp(B) =.84, p =.065). Mediation modelWe conducted mediation models with 5,000 bootstrapped resamples to examine the proposed mediation paths displayed in Figure 1 ([60]). The first models examined the paths from SES to numeracy to the three possible mediators (anxiety, preference construction, and decision uncertainty) to larger default effects. When examining these three mediators in parallel, there was a significant indirect effect through anxiety, consistent with partial mediation through anxiety (ab = −.01, 95% CI = [−.027, −.001]). This reflected a positive relationship between SES and numeracy (b =.39, 95% CI = [.28,.50]), a negative relationship between numeracy and anxiety (b = −.29, 95% CI = [−.42, −.16], and a positive relationship between anxiety and larger default effects (b =.11, 95% CI = [.04,.18]). (A second indirect effect through preference construction was significant when examined without the other two mediators, but not in a parallel mediation model with the other two mediators. There was no significant indirect effect through uncertainty, contrary to expectation.) The analogous indirect effects through financial literacy rather than numeracy revealed very similar results (see the Web Appendix). Although there was no direct effect of SES on the size of default effects in Study 5 (unlike the previous studies), we nonetheless found support for the proposed indirect effect through numeracy and anxiety. This is consistent with our conceptual framework, though, like any mediation analysis, it should be interpreted with caution because mediation analyses cannot conclusively determine whether a mediator causes an effect. DiscussionIn Study 5, we examined whether the moderators of default effects observed in Studies 1–4 would generalize to a markedly different sample (MBA students). Consumers with lower financial literacy and numeracy were more impacted by defaults, and the mediation model was consistent with our theoretical explanation (see Figure 1) of these default effect moderators. General DiscussionAcross several studies, nudges not only influenced decision making on average but also influenced choice disparities across consumers. Low-SES consumers were impacted more by nudges, meaning that nudges that facilitated selection of a good option benefited them more than high-SES consumers. Domain knowledge and numeracy also moderated the effects of nudges: Consumers with less domain knowledge and lower numeracy were impacted more by nudges compared with those with more domain knowledge and higher numeracy.These results generalized across a wide variety of consumption contexts. In addition, the effects were sizable. Across studies, nudges typically had two to five times greater impact among consumers with below-average SES, domain knowledge, and numeracy compared with consumers with above-average SES, domain knowledge, and numeracy. These results remained strong in incentivized decisions and across a series of preregistered robustness tests in which we adjusted for survey engagement, attention check failures, and alternative explanations of our results.In our studies, we sought to use decisions in which one option was best for essentially all consumers (even those with low SES and few liquid assets). The results of Study 1 were consistent with this assumption. We provided participants with the outcomes of options in Study 1 based on their actual age and liquid assets and asked them which outcome would leave them better off (see the Web Appendix). The vast majority of consumers, including those with low SES and few liquid assets, selected the options facilitated by the good nudges of saving more for retirement and making a full credit card payment as more beneficial than the other options.Because we tested the moderators of nudges across several contexts, it was possible to examine whether domain-specific skills and knowledge drive these effects. Financial literacy moderated nudge effects in the context of consumer financial decisions but not COVID-19 health decisions. In the context of the COVID-19 health decisions, health numeracy significantly moderated default effects, whereas general numeracy was not a significant moderator. These findings provide evidence that skills and knowledge moderate the effects of nudges primarily in the particular contexts in which those skills and knowledge are relevant. Implications for Marketing and PolicyNudges have become pervasive in marketing firms and policy circles because of their low costs and large average impact ([ 7]). Our results demonstrate that, beyond improving decisions on average, good nudges can reduce disparities. Because nearly every standard of ethics endorsed by governments and corporations places value on equality and reducing inequities (e.g., [66]), this provides a strong reason to use nudges.In addition, our findings have implications for nearly any marketing manager or online retailer. Choice architecture is an unavoidable aspect of online retail. For example, retailers must present products in some order, whether they ultimately choose to present products with highest ratings, lowest prices, highest sales volumes, or highest profit margins first ([72]; [75]). At checkout, retailers can choose to set the default to be the product with no insurance, no add-ons, and the least expensive shipping option, or other options can be preselected that might increase revenue. The results of the present studies suggest that these choice architecture decisions not only impact consumers' choices on average but can help reduce choice disparities. Many marketing managers try to reduce inequity and invest in expensive efforts to do so ([41]). For example, some marketing firms reduce their prices for the poor or offer financial assistance to expand access to their products and reduce inequities. Because nudges are low-cost interventions and can promote options in the mutual best interest of consumers and firms ([ 7]), the present results suggest that nudges may be an inexpensive alternative way for firms to help the poor.The present results also suggest that policy makers and firms need to carefully monitor the impact of their choice architecture tools on different segments of the population. Scholars have recently argued that it is important that researchers and policy makers understand heterogeneous effects of nudges across people ([74]). This can allow policy makers to design interventions that are effective even if they do not impact all consumer segments or groups of the population. Heterogeneity in nudge effects might also partly explain why some nudges have had smaller effects when applied and implemented at scale by policy makers or firms than when examined by researchers ([ 4]; [21]). In addition, our replications of the key results across studies and contexts addresses recent calls for researchers to replicate nudge effects ([ 4]) and examine effects of the context ([74]) to make results more useful to practitioners.Similarly, understanding heterogeneity can help marketing firms and retailers target consumer segments that would be most impacted by nudges. For example, nudges that present options with lowest unit prices first might increase purchases among low-knowledge consumers who are less familiar with the brand more than high-knowledge consumers. In some cases, managers who ignore heterogeneity in nudge effects might underestimate the effectiveness of nudges if, for example, the low-knowledge consumers most impacted by nudges include many new customers who will continue to purchase the brand in the future. In other cases, managers who are unaware of this heterogeneity might overestimate nudge effects if, for example, nudges influence one-time purchases from low-knowledge consumers rather than high-knowledge repeat customers with greater customer lifetime value. Because low-SES consumers are most impacted by nudges, this may suggest that nudges will be less successful among luxury retailers and anyone with high-SES clientele, compared with retailers catering to low-SES clientele.When only a one-size-fits-all nudge is available, our results suggest that policy makers should focus on the needs and potential benefits for low-SES and low-knowledge citizens when deciding which option to facilitate with the nudge. Nudges have less influence on high-SES and high-knowledge individuals, and it is reasonable to focus on the policies that will benefit those most impacted. For example, if one health care plan is optimal for low-SES people while another option is better for high-SES people (and only a one-size-fits-all nudge is possible), choice architects should prioritize the needs of low-SES individuals when choosing the default option. Limitations and Future ResearchAlthough we think choice architecture interventions are important tools that can reduce disparities, they should not be the only tools used to address them. Many disparities are systemic and deeply entrenched for historical, societal, or macroeconomic reasons and require interventions that change laws or elements of the macroeconomy ([24]; [45]). In addition, interventions that use incentives or provide new information can be an effective supplement to nudges ([45]). Nudges can be part of a solution that reduces disparities, but they are not enough by themselves.Across our studies, we found that the moderating effect of SES was consistent in a wide variety of contexts including consumer product choices that contained no calculations and no correct answer. Of course, it is possible that these effects do not generalize to decisions in every context. The findings might not generalize to cases in which the nudged behavior is deeply constrained ([61]). For example, healthy eating nudges might be ineffective if many low-SES consumers live in food deserts, where healthy food is difficult to obtain or expensive. It is also possible that the moderating effects would be smaller or absent for decisions in which knowledge is irrelevant or in which numbers, calculations, and ambiguity are absent. Similarly, if low-SES consumers have strong preferences and more expertise than others within a particular domain, the effect in which nudges impact low-SES consumers most might not generalize to that domain.Future research should also examine the mechanisms underlying the nudge moderators in more detail. Mediation models were consistent with our framework in which low-SES consumers are more impacted by nudges because they score lower in domain-specific skills such as numeracy (not because they allocate time or attention differently; cf. [69]). There were also indirect effects through anxiety in Study 5 (but not through uncertainty). Future investigations could expand on this by manipulating psychological processes and by examining different forms of confidence and uncertainty (e.g., [25]). Moreover, future work should examine the extent to which subjective rather than objective knowledge accounts for the effects. It is possible that people with low subjective knowledge would be greatly impacted by nudges even if they have high objective knowledge. Future work could also examine why anxiety plays a role in the differential nudge effects. For example, low-income individuals often feel stigmatized and anxious about confirming a negative ability stereotype (e.g., [76]), which might account for any effects of anxiety on nudge effects.Of course, though we manipulated choice architecture, we cannot conclude that SES, financial literacy, or numeracy caused consumers to be less susceptible to nudges, because we did not manipulate these variables. Some researchers have manipulated temporary scarcity or perceived social class (e.g., [69]). However, we would not expect these manipulations to increase nudge effects because they do not operate through our proposed mechanisms of financial literacy, numeracy, and anxiety. ConclusionWhen signing copies of his book Nudge, Richard Thaler often writes ""Nudge for good,"" encouraging readers to use nudges to benefit people rather than to increase profits at the expense of consumer welfare. The present investigation suggests that ""nudging for good"" not only helps consumers overall but also reduces inequities. The implications are clear for anyone interested in reducing inequities: nudge for good. " 18,Do Promotions Make Consumers More Generous? The Impact of Price Promotions on Consumers' Donation Behavior," Despite growing concerns regarding the increasing consumerism related to promotions, this research documents a positive effect of price promotions on consumers' donation behavior. Specifically, the authors propose that price promotions increase consumers' perceived resources, which in turn increase consumers' donation behavior. A series of seven studies, combining field and experimental data, provide converging support for this proposition and its underlying mechanism of perceived resources. Furthermore, the authors show that the positive effect of price promotions on consumers' donation behavior is attenuated when consumers focus on the amount of money spent (rather than saved), when consumers feel they have overspent their budget, and when the monetary savings cannot be realized immediately. Finally, the authors show that this effect is stronger when donation solicitation occurs immediately after the price promotion (vs. after a delay). This research documents a novel behavioral consequence of price promotions and uncovers a mechanism by which price promotions can lead to positive social consequences and contribute to a better world.","As an effective strategy for attracting consumers and increasing sales, price promotions are undoubtedly one of the most important marketing tools. In the last decade, price promotion events, such as Black Friday and Cyber Monday in Western countries as well as Alibaba's Double 11 Singles' Day in China, have become increasingly popular and yielded record-setting sales for the companies involved. For example, in 2019, U.S. consumers spent $7.4 billion on Black Friday and $9.4 billion on Cyber Monday ([35]). In the same year, the Chinese e-commerce giant Alibaba achieved a sales volume of more than $38.3 billion on Singles' Day alone ([33]).Despite the large sales volume achieved in price promotion events, some criticize the events for promoting consumerism and materialism. For example, the media has portrayed Black Friday as ""America's greediest holiday"" ([25]). As a result, several retailers and organizations call for boycotts on Black Friday and Cyber Monday sales ([22]), arguing that Black Friday not only ruins the spirit of Thanksgiving but also decreases social welfare by making consumers stingier and more selfish. An important question thus arises: Can price promotions lead to any positive social consequences? In the current research, we aim to address this question in the context of donation behavior.While a large body of research has examined the effect of price promotions on firm performance and consumers' purchasing behaviors, the existing research is relatively silent on whether and how price promotions have important social consequences. In this research, we demonstrate that price promotions actually can have a positive impact on consumers' donation behavior via an increase in consumers' perceived resources. A series of seven studies, combining field and experimental data, offer converging support for this proposition. Providing support for the perceived resources mechanism, we show that the effect of price promotions on donation behavior is moderated by whether consumers focus on the money spent rather than saved, whether consumers feel they have overspent their budget, and whether the monetary savings can be realized immediately. Furthermore, we show that the increase in perceived resources dissipates over time, and thus, the positive effect of price promotions on donation behavior is stronger when donations are solicited immediately after the price promotion. We also demonstrate the external validity of this effect in two field surveys among actual shoppers as well as two field experiments.This research makes important theoretical and practical contributions. From a theoretical perspective, our findings contribute to the price promotion literature by shedding light on the positive social consequences of price promotions. To the best of our knowledge, this is the first research to examine how price promotions can facilitate consumers' donation behavior. While the cause-related marketing literature has studied promotions and donation behavior together, it has focused narrowly on charitable donations as the promotion (e.g., [56]; [59]). By contrast, the present research focuses on donation behavior as a consequence of promotions. Furthermore, while researchers have examined various factors that influence consumers' donation behavior, price promotion (to the best of our knowledge) has never been considered one of those factors. This research thus adds to the donation behavior literature by identifying price promotion as a novel situational factor that can drive consumers' donation behavior.From a practical perspective, this research provides pertinent and actionable implications for both charitable organizations and firms. Charitable organizations can optimize their campaigns by choosing strategic targets and timing for donation solicitations—specifically, charities should target consumers who are taking part in promotions (because this group of consumers is easy to identify and is more likely to donate than the general population) and should solicit donations immediately after a price promotion event. For firms, price promotions may be great opportunities to raise funds for charitable causes, in the spirit of corporate social responsibility, by soliciting donations right after consumers have made their purchases. In traditional cause-related marketing practices, consumers might doubt a firm's prosocial motivation because its charitable donations depend on whether consumers purchase the products ([18]; [24]). Our findings suggest that firms can overcome this negative inference and enhance their corporate social responsibility image by soliciting donations after consumers make their purchases.In the following section, we review the relevant literature and derive our hypotheses for the mechanism by which price promotions can influence consumers' donation behavior. Finally, we articulate implications of our findings in the ""General Discussion"" section. Literature Review Price PromotionsThe effects of price promotions have been studied extensively from both the marketer and consumer perspectives. From the marketers' perspective, the most obvious benefit of a price promotion is that it can increase sales ([11]). This increase of sales caused by price promotions is attributed to two major mechanisms: purchase acceleration, in which consumers purchase promoted products sooner or in larger quantities, and brand switching, in which consumers switch to promoted brands of higher quality ([ 8]). However, price promotions may also entail potential long-term risks for firms. Specifically, stockpiling may preempt future sales ([ 1]); frequent promotion events may increase consumers' price sensitivity ([45]) and may also undermine firms' brand equity ([65]).From the consumers' perspective, they can benefit from price promotions in several ways. First, consumers can derive utilitarian benefits, such as monetary savings and the opportunity to upgrade to higher-quality products ([11]; [15]). Second, consumers can derive hedonic benefits, such as happiness or enjoyment, from saving money ([ 6]; [15]; [46]). Third, by participating in price promotions, consumers may construct a positive self-perception as a smart shopper ([51]).Price promotions may also induce certain negative responses from consumers. For example, consumers may make negative inferences about a discounted product (e.g., that it must be low-quality; [17]; [48]). As a result, some consumers might be reluctant to make purchases during price promotions ([ 5]). In line with this notion, [12] showed that the sales volumes of indulgent products (e.g., ice cream) actually decrease when there is a small price discount (e.g., <10% off). In addition, prior research suggests that price promotions can reduce perceived product efficacy ([53]) and negatively influence consumers' enjoyment of postpurchase consumption ([40]). Moreover, a price promotion can prompt unplanned or impulsive purchases ([31]), which can induce negative emotions such as guilt ([46]). More recently, [52] showed that mere exposure to price promotions can cause consumers to act more impatiently in unrelated domains.Although robust research has studied the impact of price promotions on firms' performances, consumers' purchasing behaviors, and consumption experiences, there is a gap in the literature on the social consequences of price promotions. In this research, we aim to address this gap by examining whether and how price promotions influence consumers' donation behavior. Antecedents of Donation BehaviorPrior research has suggested that consumers' donation behavior can be driven by both individual and situational antecedents. In terms of individual factors, moral identity (e.g., [41]; [49]; [63]), gender identity ([64]), and self-construal ([21]) have been studied as important predictors of donation behavior.In addition, prior research has examined various situational factors that influence donation behavior, such as positive mood (e.g., [32]; [44]), discrete emotions such as guilt and love (e.g., [ 7]; [14]), mortality salience (e.g., [13]), and social norms (e.g., [60]). In the current research, we contribute to the donation behavior literature by identifying price promotion as a novel situational antecedent of donation behavior. Conceptual Development and HypothesesIn this research, we examine the effect of price promotions on consumers' donation behavior. Specifically, we propose that price promotions can increase consumers' perceived resources, and the perception of greater resources, in turn, can increase consumers' donation behavior. We elaborate on our conceptual framework to derive our hypotheses in the following subsection. The Impact of Price Promotions on Consumers' Perceived ResourcesIn this research, we define ""perceived resources"" as consumers' perception of their current monetary resources. Such a perception can be influenced by both objective factors (e.g., one's actual monetary resources) and subjective factors (e.g., the subjective experience of saving money). Price promotions can work on both levels.First, price promotions can offer actual monetary savings. If consumers have already mentally budgeted their purchase expenses, then a price promotion (e.g., a discount on the promoted product or an offer for more of the same product for free) reduces consumers' expenses, thus freeing up monetary resources for other purposes ([11]; [15]). This is especially true for unexpected promotions (e.g., surprise coupons). In line with this notion, prior research suggests that consumers perceive unexpected savings from buying products on sale as windfall gains ([ 4]; [31]). This phenomenon is also referred to as a ""psychological income effect"" ([31]). Thus, the actual monetary savings from price promotions should prompt consumers to perceive an increase in resources.Second, price promotions can increase consumers' subjective experience of saving money, which is derived from a comparison with a reference price ([27]; [43]; [57]). For example, transaction utility theory ([57]) suggests that consumers gain transaction utility when they compare the price they pay with a reference price. In price promotions, the regular price is usually offered as a reference price, causing consumers to perceive that they are saving money. Consistent with transaction utility theory, prior research has shown that consumers perceive savings in price promotions when they compare the discounted price with the higher original price ([10]). Various marketing efforts (e.g., strategic display of a higher manufacturer's suggested price) can also increase consumers' perceived savings in price promotions ([37]). In no-promotion situations, in contrast, an external reference price is usually not available, so there are no salient perceived savings. Therefore, compared with no-promotion situations, participating in price promotions can make consumers believe they are saving money. This subjective experience of saving money can also increase consumers' perceived resources. Synthesizing these two factors, we propose that price promotions can boost consumers' perceived resources. Perceived Resources and Consumers' Donation BehaviorThe amount of resources that consumers have available to allocate is an important driver of donation behavior ([ 9]; [20]; [54]). Because donation behavior requires consumers to direct resources away from the self and toward others, consumers should be more (vs. less) likely to engage in donation behavior when their actual resources are abundant (vs. scarce). In line with this notion, [36] conducted a large-scale test and found an overall positive relationship between social class and donation behavior. Similarly, [ 3] used a field experiment to show that rich (vs. poor) households are more likely to behave prosocially by returning ""misdelivered"" envelopes. Nevertheless, the literature contains mixed empirical evidence on how a consumer's actual resources affect their donation behavior. For example, [47] found that people from lower socioeconomic classes or with less power are more motivated to help others in need.In this research, we focus on the amount of perceived (not actual) resources, which has a more established positive relationship with donation behavior ([28]; [50]; [62]). For example, [28] and [62] showed that, regardless of actual financial resources, people who perceive their financial situation as more abundant (vs. scarce) are more generous in their donations. In another stream of literature on the effects of resource scarcity on consumer behavior, [50] showed that perceived resource scarcity can trigger a competitive orientation and reduce consumers' likelihood of donating to charities. Similarly, [42] suggested that a perceived resource deficiency causes consumers to donate less to charities. These findings lead to our prediction that an increase in perceived resources should promote donation behavior.Note that the donation behavior literature distinguishes between donation rate and donation amount. The existing literature on the relationship between resources and donation behavior has documented a consistently positive impact of resources on donation amount and an inconsistent impact of resources on donation rate (for a review, see [61]]). In the current research, we examine donation behavior by analyzing both the donation rate and donation amount. HypothesesIn this research, we define perceived resources as consumers' perception of their current monetary resources. Building on the aforementioned discussion, we propose that price promotions can increase consumers' perceived resources, and the greater perceived resources, in turn, increase consumers' donation behavior. More formally, H1: Price promotions increase consumers' donation behavior. H2: The positive effect of price promotions on donation behavior is mediated by an increase in perceived resources.As discussed previously, the positive effect of price promotions on perceived resources can come from two sources: actual monetary savings and a subjective experience of saving money. The relative importance of these two driving forces may differ across situations. Specifically, consumers experience actual monetary savings when they have already decided to make the purchase and the promotion comes as a surprise. In such cases, consumers perceive the saved money as windfall gains. However, the subjective experience of saving money depends on the comparison with a salient reference price. Thus, when a promotion is unexpected, consumers may experience a greater increase in perceived resources due to both actual monetary savings and a subjective experience of saving money. By contrast, when a promotion is expected, consumers do not benefit objectively from actual monetary savings, but they may still experience a subjective experience of saving money. In the current research, we examine both expected and unexpected promotions. Furthermore, because the definition of perceived resources in this research pertains to consumers' perception of their current monetary resources, consumers are less likely to experience an increase in their perceived resources when the monetary savings from the promotion cannot be realized immediately.Thus, we propose that the positive effect of price promotions on consumers' donation behavior should be attenuated ( 1) when consumers are made to think about how much money they have spent rather than saved, ( 2) when consumers feel they have overspent their budget, and ( 3) when the monetary savings from the promotion cannot be realized immediately. Next, we discuss how each of these factors influences consumers' perceived resources in more detail. Of course, there are probably other conceptually relevant moderators that we do not explore here, and we encourage future research to test other possibilities. Focus on money spentConsumers have greater perceived resources after participating in price promotions because monetary savings from promotions are often more salient to consumers ([10]; [37]). Following this logic, the salience of monetary savings should be disrupted if consumers are prompted to focus on the money they have spent on their purchases. In such cases, consumers' perceived resources are less likely to increase because the subjective experience of saving money is weaker. Instead, consumers may realize that their current monetary resources have been diminished by the recent purchases. H3: The positive effect of price promotions on consumers' donation behavior is attenuated when consumers are made to think about how much money they have spent (vs. saved) on their purchases. Budget overspendingAs discussed previously, consumers' subjective experience of saving money in price promotions depends on the comparison with a higher reference price (e.g., regular price). However, such subjective perception can be influenced by a different reference price: mental budget. Specifically, consumers tend to track their expenses against their mental budget ([30]; [55]). When consumers feel that they have overspent their budget, their subjective experience of saving money is reduced as they realize that they have spent more money than they should. In such situations, their perceived resources are less likely to increase. H4: The positive effect of price promotions on donation behavior is attenuated when consumers feel that they have (vs. have not) overspent their budget. Realization of monetary savingsAs we have mentioned, the monetary savings from promotions cause consumers to perceive an increase in their current monetary resources. However, the savings from price promotions are not always immediate. For example, stores often issue rebates or rebate coupons that consumers cannot use until their next purchase. In such situations, consumers' current expenditures are unaffected by the promotion, so their perceived resources are unlikely to increase until they realize those monetary savings in the next purchase. Therefore, the positive effect of price promotions on consumers' donation behavior should be attenuated when the monetary savings cannot be realized immediately. H5: The positive effect of price promotions on donation behavior is attenuated when the monetary savings cannot be realized immediately (e.g., future rebates).In addition to the aforementioned three moderators that influence the relationship between price promotions and consumers' perceived resources, we identify another managerially relevant moderator that can influence the relationship between perceived resources and donation behavior. Time interval between the price promotion and donation solicitationAfter consumers participate in a price promotion, their increase in perceived monetary resources—and subsequent increase in donation behavior—should diminish over time for two main reasons. First, if consumers actually saved money in the promotion, they may spend that money on other consumption alternatives in the near future. Second, consumers may gradually adapt to the subjective experience of saving money as time passes after the promotion event ([26]; [58]). Thus, the positive impact of the increase in perceived resources on donation behavior should dissipate with a delay between the price promotion event and donation solicitation. H6: The positive effect of price promotions on donation behavior is stronger when the donation is solicited immediately after the price promotion and is attenuated when there is a delay. Overview of StudiesWe tested our hypotheses in seven studies (for an overview of our conceptual framework and studies, see Figure 1). Study 1 provided initial field evidence for the positive effect of price promotions on consumers' donation behavior (H1). Study 2 manipulated the presence as well as the magnitude of price promotions, provided causal evidence, and offered process support by examining the mediating role of perceived resources (H2). Study 3 provided further causal evidence in a field experiment (H1). Study 4 provided further support for the perceived resources mechanism by examining whether the effect is attenuated when consumers focus on the amount of money they spent in the promotion (H3). Study 5 examined the moderating role of budget overspending (H4). Study 6, another field experiment, examined the moderating role of the immediacy of the savings (H5). Finally, Study 7 tested the moderating role of the time interval between the price promotion and donation solicitation (H6). Across studies, we applied consistent outlier exclusion criteria (i.e., ±3 SD from the mean) in our analyses and clearly stated any additional exclusion criteria in each study (for exclusion details across studies, see Web Appendix 7). Studies 2 and 5 also examined the impact of price promotions on consumers' feelings (for supplementary analyses on these measures, see Web Appendix 5). Across the seven studies, using both field and experimental data, our findings converged to establish the robustness and external validity of the effect and to provide support for the underlying mechanism of perceived resources.Graph: Figure 1. Overview of conceptual framework and studies. Study 1: Field Evidence from Alibaba's Double 11 Sales EventAlibaba's Double 11 sales event is the largest price promotion event in China, and its online retail website, Tmall, offers big price promotions on November 11 (hence the name ""Double 11""). In Study 1, we used this opportunity to collect field evidence on the impact of price promotions on consumers' charitable behaviors. Actual donation data from two charitable organizations provided preliminary support for our hypothesis (see Web Appendix 1). To further examine the impact of the Double 11 sales event on charitable behaviors, we surveyed individual shoppers who participated and examined whether their spending during the Double 11 sales event positively predicted their charitable behaviors in the subsequent week. MethodOne hundred thirty-five shoppers who participated in the 2017 Alibaba's Double 11 sales event (44.4% female; all ≥18 years old) were recruited from an online panel in China on November 18 to complete this study in exchange for monetary compensation. Shoppers were told that the study aimed to understand consumers' shopping experience, and they completed a few ostensibly unrelated surveys. In the first survey, they were asked to indicate how much money (in RMB) they actually spent on their purchases during the sales event on a 22-point scale (1 = 0–200, 2 = 201–500,..., 21 = 40,001–50,000, and 22 = >50,000). Next, shoppers were asked to rate how much money they perceived they had saved during the sales event on a seven-point scale (1 = ""very little money saved,"" and 7 = ""a lot of money saved"").The second survey ostensibly sought to understand the shoppers' daily life experiences in the one week preceding the survey (i.e., the week after the sales event). Specifically, shoppers were asked whether they had engaged in each of 13 types of behavior in the week of November 12–18 (1 = yes, 0 = no; for the list of behaviors, see the Appendix). The responses to these 13 items were summed to form a composite charitable behaviors index. Shoppers were also asked whether they would be interested in engaging in those behaviors in the future. The responses to these 13 items were averaged to form a behavioral intention index (α =.92). Finally, shoppers completed basic demographic questions including their age range and gender; these measures were included in all subsequent studies and will not be mentioned again. Results and Discussion Perceived savingsA linear regression analysis revealed that the amount spent during the sales event had a significant, positive effect on perceived savings (b =.11, t(133) = 3.20, p =.002). In other words, the more money shoppers spent during the Double 11 sales event, the more money they perceived they had saved. Charitable behaviors during the week of the Double 11 sales eventIn a linear regression, the amount spent during the event had a significant positive correlation with engagement in charitable behaviors (b =.26, t(133) = 2.89, p =.004). A mediation analysis ([29], PROCESS Model 4 with 5,000 bootstrap samples) revealed that perceived savings mediated this effect (indirect effect =.11, SE =.05, 95% confidence interval [CI] = [.03,.22]). Intention to engage in future charitable behaviorsSimilarly, a linear regression analysis revealed a significant positive correlation between the amount spent during the Double 11 sales event and shoppers' intention to engage in charitable behaviors in the future (b =.07, t(133) = 3.29, p =.001). Again, a mediation analysis ([29], PROCESS Model 4 with 5,000 bootstrap samples) revealed that perceived savings mediated the effect (indirect effect =.02, SE =.01, 95% CI = [.01,.05]).As a robustness check, we conducted the same analyses with age and gender as covariates. Notably, controlling for age and gender did not alter the interpretation or level of significance of our results in this or any of the subsequent studies.In summary, Study 1 found that shoppers' spending during the Double 11 sales event positively predicted both their charitable behaviors in the week following the event and their intention to engage in charitable behaviors in the future. Furthermore, perceived savings from the price promotions mediated the effect of shoppers' spending on their charitable behaviors. One limitation of such field data is that it can provide only correlational evidence. For example, one could argue that shoppers who spent more during the Double 11 sales event happened to be more interested in various forms of charitable behaviors in general. In the next studies, we intend to provide causal evidence for the effect of price promotions on consumers' charitable behavior. Study 2: Manipulating the Presence and Magnitude of Price PromotionsIn Study 2, we aimed to provide causal evidence for the impact of price promotions on consumers' donation behavior by manipulating not only the presence but also the magnitude of the price promotion. Furthermore, we examined the mediating role of perceived resources. MethodTwo hundred participants in the United States (37.5% female; Mage = 34.7 years) were recruited from Amazon's Mechanical Turk to complete this study in exchange for monetary compensation. Participants were randomly assigned to one of four conditions (no promotion vs. 10% off vs. 50% off vs. 50% off with double spending).All participants were asked to imagine that they were visiting a shopping mall. In the 50%-off (10%-off) condition, participants were told that the shopping mall was having a 50% off (10% off) sales event, and they spent $500 on purchases that originally cost $1,000 ($560). In the no-promotion (control) condition, participants were simply told that they spent $500 on purchases. Finally, in the 50%-off-with-double-spending condition, participants were told that they spent $1,000 on purchases that originally cost $2,000. Because participants in the 50%-off-with-double-spending condition saved the most money, we expected that they would experience even greater perceived resources and hence would donate more to charities, relative to participants in the 10%-off and 50%-off conditions.Subsequently, all participants were told that as they walked out of the shopping mall, they noticed that United Way was raising money for the Hurricane Florence Relief Fund to support local communities in South Carolina, North Carolina, Virginia, and the surrounding areas affected by Hurricane Florence. Participants indicated the amount they were willing to donate to this charity fund. Next, participants responded to three items regarding their perceived resources after making their purchases in the shopping mall: (""After making the purchase, I feel that I have saved a lot of money,"" ""After making the purchase, I feel that I have more resources at hand,"" and ""After making the purchase, I feel that my resources are sufficient""; 1 = ""strongly disagree,"" and 7 = ""strongly agree""). Responses to these three statements were averaged to form a perceived resources index (α =.88). Finally, participants responded to two items regarding whether Hurricane Florence had affected them personally and whether it had affected their family and/or close friends (1 = ""not at all,"" and 7 = ""very much""); controlling for these two items in our subsequent analyses did not alter the interpretation or level of significance of the results (reported next). Results and DiscussionTo better understand the donation behavior behaviors in our experiments, we analyze both the donation rate and the average donation amount in each condition. In line with prior research (e.g., [63]; [64]), we report the average donation amount across all participants (i.e., including those who indicated a zero donation amount) in the article. For completeness, we also report the average donation amount among only those who donated (i.e., excluding those who indicated a zero donation amount) in Web Appendix 6. Donation rateThe percentage of participants who were willing to make a donation (i.e., who indicated a nonzero donation amount) did not significantly differ across conditions (Wald χ2( 3) = 3.73, p =.29). Participants in the no-promotion condition (65.31%) exhibited a directionally lower likelihood of donating than participants in the 10%-off condition (76.00%), 50%-off condition (66.67%), and 50%-off-with-double-spending condition (80.00%). The difference between the no-promotion condition (65.31%) and the pooled promotion conditions (74.17%) was not significant (Wald χ2( 1) = 1.57, p =.21). Donation amountGiven the large variance in the donation amount, we first identified and removed two outliers that were three standard deviations above or below the mean. In addition, the donation amount was positively skewed (skewness = 2.56, SE =.17). Thus, we log-transformed the donation amount after adding 1 to each score to include zeros in the analysis. The pattern of results remained the same regardless of whether we log-transformed the donation amount; for ease of interpretation, we report the untransformed means. An analysis of variance (ANOVA) revealed a significant main effect of the price promotion (F( 3, 194) = 3.88, p =.01, ηp2 =.06), and a trend analysis confirmed a linear trend (F( 1, 194) = 10.94, p =.001, ηp2 =.05). As Figure 2 shows, participants in the 50%-off condition (M50% = $14.92, SD = $22.09) indicated a greater donation amount than participants in the no-promotion condition (Mno promo = $7.54, SD = $11.44; F( 1, 194) = 2.89, p =.09, ηp2 =.02). More interestingly, participants in the 50%-off-with-double-spending condition indicated an even greater donation amount (M50%, double spend = $23.86, SD = $30.29) than both participants in the 50%-off condition (M50% = $14.92, SD = $22.09; F( 1, 194) = 2.99, p =.09, ηp2 =.02) and participants in the no-promotion condition (Mno promo = $7.54, SD = $11.44; F( 1, 194) = 11.52, p <.001, ηp2 =.06). The donation amount in the 10%-off condition (M10% = $11.37, SD = $13.52) fell in the middle and did not differ significantly from the no-promotion condition (F( 1, 194) = 2.01, p =.16, ηp2 =.01), though the pattern was consistent with our expectation. Furthermore, a planned contrast between the no-promotion condition and the pooled promotion conditions was significant (Mno promo = $7.54, SD = $11.44 vs. Mpromo pooled = $16.74, SD = $23.49; F( 1, 194) = 6.99, p =.01, ηp2 =.03).Graph: Figure 2. Donation amount and perceived resources as a function of price promotion (Study 2).†p <.10.**p <.01.Notes: Error bars denote ±1 SE. Perceived resourcesAn ANOVA on the perceived resources index also revealed a significant main effect of the price promotion (F( 3, 194) = 29.51, p <.001, ηp2 =.31), and a trend analysis confirmed a linear trend (F( 1, 194) = 79.81, p <.001, ηp2 =.29). As shown in Figure 2, participants in the 50%-off condition (M50% = 4.82, SD = 1.76) reported greater perceived resources than participants in the no-promotion condition (Mno promo = 2.57, SD = 1.23; F( 1, 194) = 53.78, p <.001, ηp2 =.22). Interestingly, participants in the 50%-off-with-double-spending condition (M50%, double spend = 5.29, SD = 1.47) reported even greater perceived resources than both participants in the 50%-off condition (M50% = 4.82, SD = 1.59; F( 1, 194) = 2.39, p =.12, ηp2 =.01) and participants in the no-promotion condition (Mno promo = 2.57, SD = 1.23; F( 1, 194) = 77.79, p <.001, ηp2 =.29). Participants in the 10%-off condition (M10% = 4.28, SD = 1.59) also reported greater perceived resources than participants in the no-promotion condition (Mno promo = 2.57, SD = 1.23; F( 1, 194) = 30.33, p <.001, ηp2 =.14). Furthermore, a planned contrast between the no-promotion condition and the pooled promotion conditions was also significant (F1, 194; Mno promo = 2.57, SD = 1.23 vs. Mpromo pooled = 4.80, SD = 1.65) = 77.35, p <.001, ηp2 =.29). Mediation analysisA mediation analysis ([29], PROCESS Model 4 with 5,000 bootstrap samples) on log-transformed donation amounts revealed that perceived resources significantly mediated the effect of the price promotion (1 = promotion, 0 = no promotion) on participants' donation behavior (indirect effect =.61, SE =.15, 95% CI = [.32,.93]). After controlling for the indirect effect, the direct effect of the price promotion on donation behavior was no longer significant (direct effect =.01, SE =.26, 95% CI = [−.51,.53]). A mediation analysis with price promotion as a multicategorical variable provided consistent support for the mediating role of perceived resources (for detailed results, see Table 1).GraphTable 1. Mediation Effect of Perceived Resources (Study 2). In Study 2, participants in the price promotion conditions (relative to the no-promotion condition) made a greater average donation to a charitable organization, and this positive effect increased with the magnitude of the monetary savings. Interestingly, participants in the 50%-off-with-double-spending condition reported the highest level of perceived resources—perhaps driven by both the perception that they had more money to spend and their greater monetary savings—and these participants also indicated the greatest average donation amount. Furthermore, we showed that perceived resources mediated the effect of the price promotion on donation behavior. In this study (as well as in Study 5), we also included some affective measures to examine the role of feelings such as happiness and guilt. Unlike perceived resources, these affective measures did not directly mediate the effect of price promotions on donation behavior (for supplementary analyses on these measures, see Web Appendix 5). Study 3: Manipulating Price Promotion in a Field ExperimentStudy 3 used a field experiment to examine the causal impact of price promotions on donation behavior in a more realistic setting. We conducted the field experiment in collaboration with a café, where we randomly distributed discount coupons to customers. We predicted that customers who received (vs. did not receive) a discount coupon would make more donation behavior to a charitable cause. MethodWe conducted the field experiment in a café in a large city in China in June 2020. Because there were no queues at the cashier counter of this café, we could use random assignment without the customers noticing. The field experiment followed a two-cell (promotion vs. no promotion) between-subjects design, and we introduced the promotion manipulation after customers gave their orders to the cashier. To avoid potential data contamination, we did not include subsequent orders made by repeat customers. In the price promotion condition, a 10 RMB discount coupon was applied to the customer's order. In the no-promotion condition, customers received no discount. Customers were randomly assigned to one of the two conditions. In one day (between 9 a.m. and 9 p.m.), we observed 121 orders (37.2% female customers).After each customer paid the bill, the research assistant, dressed as a staff member, handed the customer their receipt with an attached donation appeal and collected the café's copy of the receipt, which recorded the order details and the manipulation information. Meanwhile, another research assistant, disguised as a customer, recorded each customer's gender, estimated age, and number of companions.The donation appeal flyer solicited donations to help pay the medical expenses of a 28-year-old woman diagnosed with leukemia. To reduce a potential norm of reciprocity, we chose this real donation appeal, which was posted by the recipient herself on a large online charity fundraising platform. Posters with the same information were also displayed at the cashier counter (for the field experiment setting, see Web Appendix 2). The research assistant who was disguised as a staff member was blind to our hypothesis and was instructed not to interact with customers regarding the donation appeal. We attached the QR code to the donation appeal flyer, so customers could make their donation decision at their own tables privately instead of deciding at the cashier counter. Customers who intended to donate could scan the QR code printed on the donation appeal flyer to view detailed information about the donation cause and make an actual donation. To match the donation data with the experimental conditions, we asked participants to enter the last four digits of their order number after they made their donation. At the end of the day, research assistants transferred all donations to the woman on behalf of the customers.Of the 121 orders observed, 20 orders were excluded because the customer did not take the receipt and attached donation flyer. To cleanly manipulate price promotion, we also removed eight orders made by customers using other discounts and one order for which the customer refused to accept the coupon. As a result, 92 orders qualified for our analysis. Results and Discussion Donation rateFirst, we examined the percentage of customers who donated in each condition. A binary logistic regression revealed that customers in the promotion condition were more likely to donate (17.50%) than those in the no-promotion condition (1.92%; Wald χ2( 1) = 4.75, p =.03). Next, we examined whether having a companion affected the customer's donation decision. When included as a covariate in our analysis, having a companion did not have a significant effect (Wald χ2 ( 1) = 2.45, p =.12), and the effect of the price promotion on the donation rate remained significant (Wald χ2 ( 1) = 4.55, p =.03). Donation amountThe donation amount was positively skewed (skewness = 4.23, SE =.25). Thus, we log-transformed the donation amount, as in Study 2. The pattern of results remained the same, so we report the untransformed means for ease of interpretation. A one-way ANOVA revealed a positive effect of the price promotion on the donation amount (Mpromo = ¥1.00, SD = ¥2.52 vs. Mno promo = ¥.08, SD = ¥.56; F( 1, 90) = 7.29, p =.008, ηp2 =.07). In addition, having a companion did not significantly affect the donation amount (F( 1, 89) = 2.01, p =.16, ηp2 =.02), and the effect of the price promotion remained significant after controlling for this covariate (F( 1, 89) = 6.78, p =.01, ηp2 =.07).The results of this field experiment thus provided strong causal evidence for the positive effect of price promotions on donation behavior. Specifically, we found that a discount at a café significantly increased actual donation behavior among café customers. Study 4: The Moderating Role of Focusing on Money SpentIn the next few studies (Studies 4–6), we aimed to test the underlying mechanism of perceived resources by examining whether the positive effect of price promotions on consumers' donation behavior is attenuated if we disrupt the impact of price promotions on consumers' perceived resources. In Study 4, we examined whether directing consumers' focus to the amount of money they just spent would reduce their perceived resources and hence attenuate the positive effect of price promotions on donation behavior. MethodThree hundred thirty-five students from a large university in Singapore (65.1% female; Mage = 22.4 years) completed an online experiment in exchange for a chance to win SGD $20. The experiment followed a 2 (promotion vs. no promotion) × 2 (control vs. focus on money spent) between-subjects design, and participants were randomly assigned to one of the four conditions.As a cover story, we told participants that we were interested in their preferences for food delivery services. Participants were asked to imagine that they had been endowed with $20 and were instructed to use some of it to purchase a voucher; participants then had a choice to donate some of the leftover endowment to a charity. To make the experiment incentive-compatible, we followed recent research ([ 2]) and informed participants that we would randomly select ten participants and actually fulfill their decisions (i.e., they would receive their chosen voucher and any remaining endowment that did not go to the charity). To avoid a selection issue (i.e., that participants in the promotion condition might be more likely to make a purchase), we required participants to make a purchase choice between two product options.In the no-promotion condition, participants were told that they could purchase a $10 voucher from either Grabfood or Foodpanda, two popular food delivery companies in Singapore. In the promotion condition, participants were told that they could purchase a $20 voucher for $10 (50% off) from either of these two companies. We controlled the final purchase price ($10 in both conditions) to eliminate the potential concern that participants in the promotion condition would spend less money on the required task and thus have more disposable money for a donation. After making the purchase decision, participants in the focus-on-money-spent condition were asked to recall how much money they spent on the voucher, whereas those in the control condition did not receive this instruction.Subsequently, all participants took a survey in which they were asked to indicate how much they were willing to donate (between $0 and $10) to the Children's Charities Association to help physically, mentally, and socially disadvantaged children in Singapore. Participants were told that they would be expected to send their indicated donation amount if they won the lottery. Finally, they completed the same perceived resources scale (α =.89) used in Study 2.After completing the study, all participants provided their email addresses for the lottery draw. Ten randomly chosen participants received the cash prize and were given the donation method to fulfill their promise to the charity. Results and Discussion Donation rateA binary logistic regression analysis on the donation rate revealed a significant interaction effect between the price promotion and focus manipulation (Wald χ2( 1) = 6.86, p <.01). There was also a significant main effect of the focus manipulation (Wald χ2( 1) = 12.35, p <.001), whereas the main effect of the price promotion was not significant (Wald χ2( 1) = 1.48, p =.22). Within the control condition, participants in the promotion condition were more likely to donate to the charity (95.35%) than those in the no-promotion condition (84.34%; Wald χ2( 1) = 5.06, p =.02). This effect was eliminated in the focus-on-money-spent condition (promotion: 70.73% vs. control: 79.76%; Wald χ2( 1) = 1.80, p =.18). Donation amountNo outliers were identified for donation amount, and there was no skewness problem (skewness = −.13, SE =.13). An ANOVA with the price promotion and focus manipulation as independent variables and donation amount as the dependent variable revealed a significant interaction effect (F( 1, 331) = 5.54, p =.02, ηp2 =.02). There was also a significant main effect of the focus manipulation (F( 1, 331) = 17.42, p <.001, ηp2 =.05), whereas the main effect of the price promotion was not significant (F( 1, 331) =.75, p =.39, ηp2 =.002). Within the control condition, the average donation amount was greater in the promotion condition (Mpromo = $7.12, SD = $3.31) than in the no-promotion condition (Mno promo = $5.80, SD = $3.83; F( 1, 331) = 5.22, p =.02, ηp2 =.02). Within the focus-on-money-spent condition, however, the effect of the price promotion on the donation amount was eliminated (Mpromo = $4.44, SD = $3.98 vs. Mno promo = $5.05, SD = $3.88; F( 1, 331) = 1.10, p =.30, ηp2 =.003). Perceived resourcesAn ANOVA on the perceived resources index revealed a significant main effect of the price promotion (Mpromo = 4.86, SD = 1.27 vs. Mno promo = 4.05, SD = 1.46; F( 1, 331) = 29.21, p <.001, ηp2 =.08) and a significant main effect of the focus manipulation (Mcontrol = 4.62, SD = 1.52 vs. Mfocus = 4.29, SD = 1.43; F( 1, 331) = 4.56, p =.03, ηp2 =.01). However, the interaction term was not significant (F( 1, 331) = 1.05, p =.31, ηp2 =.003).The results from this study replicated the previous findings using an incentive-compatible online experiment. Furthermore, we found that the positive effect of the price promotion on donation behavior was eliminated when consumers were guided to think about the money they spent in the promotion. However, we did not observe a significant interaction effect on the perceived resources index. We suspect that although the focus manipulation significantly reduced participants' subjective experience of saving money, participants in the promotion condition had more actual monetary resources (i.e., a voucher worth $20) than those in the no-promotion condition (in which the voucher was worth only $10). In the next study, we address this limitation by using a cleaner manipulation. Study 5: The Moderating Role of Budget OverspendingStudy 5 aimed to test budget overspending as a moderator of the effect of price promotions on donation behavior. Specifically, we predicted that the positive effect of price promotions on perceived resources (and, subsequently, on donation behavior) would be attenuated when the purchase exceeded consumers' mental budget. MethodFive hundred fourteen undergraduate students in China (43.8% female, all ≥18 years old) from an online subject pool participated in this study for monetary compensation. The study followed a 2 (promotion vs. no promotion) × 2 (over budget vs. within budget) between-subjects design.All participants imagined that they were browsing one of their favorite online shops. In the promotion conditions, participants were told that the online shop was having a 50% off sale, and they spent ¥500 on products that originally cost ¥1,000. In the no-promotion conditions, participants were told that they spent ¥500 in the online shop. In the over-budget (within-budget) conditions, participants were told that their spending exceeded (was within) their budget. Subsequently, participants imagined that when they paid the bill through Alipay, they noticed that the platform was raising money to plant trees in a remote area in China. The cost of planting each tree was ¥3, and each participant could donate a maximum of ten trees. Participants indicated the number of trees they were willing to donate. Finally, they completed the same perceived resources scale (α =.79) used in Study 2. Results and Discussion Donation rateAcross conditions, a large majority of participants were willing to donate at least one tree (within-budget/promotion: 98.44%, within-budget/no-promotion: 96.12%, over-budget/promotion: 93.80%, over-budget/no-promotion: 93.75%). A binary logistic regression analysis on the donation rate revealed only a significant main effect of budget overspending (Wald χ2 ( 1) = 3.79, p =.05); neither the main effect of the price promotion nor the interaction effect was significant (ps >.34). Number of trees donated to charityGiven that the dependent variable was count data, we conducted a Poisson regression (e.g., [16]; [38]) on the number of trees donated, with budget overspending and the price promotion as independent factors. The analysis revealed a significant main effect of the price promotion (Wald χ2( 1) = 3.74, p =.05), no significant main effect of budget overspending (Wald χ2( 1) =.25, p =.62), and most importantly, a significant interaction effect (Wald χ2( 1) = 3.71, p =.05). Specifically, within the within-budget condition, the average donation amount was greater in the promotion condition (Mpromo = 6.07, SD = 3.20) than in the no-promotion condition (Mno promo = 5.26, SD = 3.37; Wald χ2( 1) = 7.38, p =.007). This effect did not occur within the over-budget condition (Mpromo = 5.76, SD = 3.32 vs. Mno promo = 5.76, SD = 3.41; Wald χ2 ( 1) <.001, p =.99). Perceived resourcesAn ANOVA on perceived resources revealed a significant main effect of the price promotion (F( 1, 510) = 44.30, p <.001), a significant main effect of budget overspending (F( 1, 510) = 26.21, p <.001), and most importantly, a significant interaction effect (F( 1, 510) = 7.27, p =.007). Specifically, among the within-budget participants, those in the promotion condition reported greater perceived resources (Mpromo = 5.27, SD = 1.11) than those in the no-promotion condition (Mno promo = 4.20, SD = 1.30; F( 1, 510) = 43.73, p <.001). This effect was attenuated among the over-budget participants (Mpromo = 4.38, SD = 1.31 vs. Mno promo = 3.93, SD = 1.44; F( 1, 510) = 7.84, p =.005). Moderated mediation analysisNext, we tested whether perceived resources mediated the effect of the price promotion on the donation amount and whether budget overspending moderated the path from the price promotion to perceived resources. A moderated mediation analysis ([29], PROCESS Model 7 with 5,000 bootstrap samples) with perceived resources as the mediator and budget overspending as the moderator revealed a significant index of moderated mediation (b = −.57, SE =.22, 95% CI = [−1.01, −.15]). Specifically, in the within-budget conditions, the indirect effect of the price promotion on donation via perceived resources was positive and significant (b =.98, SE =.16, 95% CI = [.68, 1.32]). However, in the over-budget conditions, this indirect effect was attenuated (b =.42, SE =.16, 95% CI = [.11,.75]).Results from Study 5 provided further support for the underlying mechanism related to perceived resources. Specifically, the study showed that budget overspending attenuates the positive effect of price promotions on consumers' donation behavior via its influence on perceived resources. To further test the moderating role of budget overspending, we conducted an additional study in which we manipulated the product type. Prior research suggests that consumers usually have mental budgets for necessity purchases, whereas indulgence purchases often entail budget overspending ([34]; [55]). This additional study followed a 2 (promotion vs. no promotion) × 2 (necessity purchase vs. indulgence purchase) between-subjects design, and it is reported in Web Appendix 3. Study 6: The Moderating Role of the Immediacy of the SavingsThe objective of Study 6 was twofold. First, we aimed to provide further field evidence for the effect of price promotions on consumers' donation behavior. To this end, we collaborated with a café to run price promotions, and we examined customers' actual donation behavior. Second, we wanted to examine the perceived resources mechanism by comparing the effects of an instant discount coupon versus a rebate coupon. Specifically, consumers who received a rebate coupon could use the money to offset their spending in the next (but not the current) purchase. In other words, the rebate coupon did not offer immediate monetary savings, so the rebate coupon (unlike the discount coupon) should not increase consumers' current perceived resources. Therefore, we predicted that consumers who received the instant discount coupon would exhibit greater donation behavior than those who received the rebate coupon or no coupon. MethodWe conducted this field experiment in a café on the campus of a large university in China in October 2019. The field experiment followed a three-cell between-subjects design (no promotion vs. instant discount coupon vs. rebate coupon). To minimize the risk that customers would notice the existence of other conditions, which could potentially cause data contamination, we did not randomize the conditions within a day. Instead, we conducted the study over the course of six days (Tuesday, Wednesday, and Thursday for two consecutive weeks) and randomly assigned one experimental condition to each day (yielding two days per condition). In total, we observed 399 customers (48.6% female), with 107, 127, and 165 customers in the no-promotion, instant-discount-coupon, and rebate-coupon conditions, respectively.The field experiment commenced when customers came to order food/drinks from the cashier. In the no-promotion condition, customers did not receive any kind of coupon. In the instant-discount-coupon condition, the cashier gave each customer a ¥5 coupon (valid until November 30) and told them that they could use the coupon on their current order. In the rebate-coupon condition, the cashier gave each customer a ¥5 coupon (valid until November 30) and told them that the coupon was valid only on future purchases. Customers paid and then waited at the pick-up counter for their orders; as they waited, a research assistant approached them with a donation appeal poster for House of Kindness, a charity run by the university's student union to help replace broken free-sharing umbrellas provided for students and teachers on the campus. The same poster was also displayed at the café's pick-up counter (for the field experiment setting, see Web Appendix 4). The research assistant asked each customer to consider donating, and customers who agreed proceeded to donate any amount by scanning the QR code on the poster. At the same time, another research assistant, who pretended to be a server in the café, observed and recorded each customer's gender, estimated age, number of companions, and donation behavior (yes/no), and she collected each customer's receipt when they picked up their order. Results and Discussion Donation rateA binary logistic regression on the donation rate revealed a marginally significant effect of the price promotion (Wald χ2( 2) = 4.52, p =.10). Specifically, the donation rate in the instant-discount-coupon condition (52.76%) was higher than in both the rebate-coupon condition (40.61%; Wald χ2( 1) = 4.24, p =.04) and the no-promotion condition (42.99%; Wald χ2( 1) = 2.21, p =.13). Importantly, there was a significant difference between the instant-discount-coupon condition (52.76%) and the other two conditions combined (41.54%; Wald χ2( 1) = 4.11, p =.04). The results remained robust after controlling for the presence of a companion (Wald χ2( 2) = 4.86, p =.09), which had an insignificant effect (Wald χ2( 1) =.54, p =.46). Donation amountThe donation amount varied from ¥0 to ¥100. Due to the high variance, we identified and removed six outliers that were three standard deviations above or below the mean. The donation amount was positively skewed (skewness = 1.81, SE =.12), so we log-transformed the donation amount; the pattern of results remained the same regardless of the log-transformation, and we report the untransformed means for ease of interpretation. An ANOVA revealed an insignificant effect of the price promotion on the donation amount (F( 2, 390) = 2.03, p =.13, ηp2 =.01). Specifically, the average donation amount was directionally higher in the instant-discount-coupon condition (Minstant = ¥3.45, SD = ¥4.48) than in both the no-promotion condition (Mno promo = ¥2.87, SD = ¥4.50; F( 1, 390) = 2.12, p =.15, ηp2 =.005) and the rebate-coupon condition (Mrebate = ¥2.86, SD = ¥4.93; F( 1, 390) = 3.74, p =.05, ηp2 =.01). Importantly, there was a significant difference between the instant-discount-coupon condition and the other two conditions combined (F( 1, 390) = 3.76, p =.05, ηp2 =.01). The effect remained robust after controlling for the presence of a companion (F( 2, 389) = 2.14, p =.12, ηp2 =.01), which had an insignificant effect (F( 1, 389) =.30, p =.59, ηp2 =.001).The results of this study provided further field evidence in a real shopping environment for the positive effect of price promotions on consumers' donation behavior. Furthermore, we found that customers who received immediate monetary savings (i.e., the instant discount coupon) were more likely to donate than customers whose monetary savings were not immediate (i.e., the rebate coupon). Study 7: The Time Interval Between Price Promotion and Donation SolicitationStudy 7 investigates another boundary condition—the time interval between the price promotion and the donation solicitation—that we hypothesized would moderate the positive effect of price promotions on donation behavior. As time passes after a price promotion, consumers may spend their savings on another purchase or may gradually adapt to the savings; both outcomes should eliminate any increase in perceived resources that the promotion originally conferred. Thus, we expected that the positive effect of price promotions on donation behavior would be stronger if the donation was solicited immediately after the price promotion and would be attenuated if there was a delay between the price promotion and donation solicitation. MethodTwo hundred ninety-four shoppers who made purchases on December 12 in the 2019 Alibaba's Double 12 sales event (63.4% female, all ≥18 years old) were recruited from an online panel in China on either December 12–13 (124 shoppers) or December 20–21 (170 shoppers) to complete this study in exchange for monetary compensation. As a cover story, we told all shoppers that we were interested in understanding their shopping experience. As in Study 1, shoppers completed a few ostensibly unrelated surveys. In the first survey, they were asked to indicate how much money (in RMB) they actually spent on their purchases on the same 22-point scale used in Study 1. We did not measure perceived savings to rule out demand effects as a possible explanation for the results of Study 1.In the second survey, shoppers saw a donation appeal from the charity Wardrobe of Love, which raises funds to purchase new winter clothes for children in need. Shoppers indicated their intention to donate to this charity on a seven-point scale (1 = ""definitely will not donate,"" and 7 = ""definitely will donate""). Results and DiscussionConsistent with the findings of Study 1, a linear regression analysis revealed that the amount spent during the Double 12 sales event was significantly positively correlated with shoppers' intention to donate to the charity (b =.09, t(292) = 3.59, p <.001). More importantly, a linear regression with donation intention as the dependent variable and the amount spent and time interval (−.5 = immediately after the promotion,.5 = one week after the promotion) as independent variables revealed a significant main effect of the amount spent (b =.09, t(290) = 3.65, p <.001) and a significant main effect of the time interval (b =.76, t(290) = 3.49, p <.001). These main effects were qualified by a marginally significant interaction effect between the time interval and amount spent (b = −.08, t(290) = −1.66, p <.10).Specifically, among shoppers who were surveyed immediately after the Double 12 promotion event, the donation intention significantly increased with the amount spent in the promotion (b =.13, t(290) = 3.36, p <.001), but this effect did not occur among shoppers who were surveyed one week after the promotion event (b =.05, t(290) = 1.62, p >.10). Study 7 thus showed that the positive effect of price promotions on donation behavior is stronger when the donation is solicited immediately after the price promotion, and it disappears over time. General DiscussionAcross a set of seven studies, using both field and experimental data, we provide robust evidence that price promotions can increase consumers' donation behavior (for a summary of the results, see Table 2). Study 1 provided correlational field evidence for the proposed effect. Study 2 manipulated the presence as well as the magnitude of the price promotion and provided causal evidence for the effect. Study 2 also showed that perceived resources mediated the effect of price promotions on donation behavior. Study 3 provided further causal evidence by implementing a price promotion in a field experiment. Next, we provided further support for the perceived resources mechanism by showing that the positive effect of price promotions on consumers' donation behavior was attenuated when consumers focused on how much money they spent (rather than saved) in the promotion (Study 4), when the purchase involved budget overspending (Study 5), and when the monetary savings could not be realized immediately (Study 6). Finally, we showed that the effect was attenuated by a longer delay (one week vs. one day) between the price promotion and donation solicitation (Study 7). Together, our findings converged to establish the robustness of the positive effect of price promotions on consumers' donation behavior.GraphTable 2. Summary of the Effect of Price Promotions on Donation Behavior in Each Study. 40022242920988260 a The difference between the promotion condition and the no-promotion/other condition was significant (p <.05).50022242920988260 b The difference between the promotion condition and the no-promotion/other condition was marginally significant (p <.10).60022242920988260 c The difference between the promotion condition and the no-promotion/other condition was insignificant (p >.10).70022242920988260 d The promotion conditions were pooled to compare with the no-promotion condition.80022242920988250 e The no-promotion condition and rebate-coupon condition were pooled to compare with the instant-discount-coupon condition.It is worth noting that although we found a consistent effect on the donation amount, we found mixed results on the donation rate. This might have been partially caused by a procedural difference in the donation rate measurement: in our field experiments (Studies 2 and 6), we first asked customers whether they would like to donate; only customers who decided to donate then moved on to make a donation. In other words, it was a two-step donation decision as we explicitly asked customers to make two sequential decisions: ( 1) whether to donate and ( 2) how much money to donate. In the other studies, however, we asked all participants to indicate the donation amount, and we computed the donation rate by coding those who indicated a donation amount of ""0"" as ""didn't donate"" and those who indicated a nonzero donation amount as ""donated."" Nevertheless, these results are consistent with the prior findings that different factors may affect donation choice and donation amount (e.g., [19]; [23]).Furthermore, we should acknowledge that the effect of price promotions on donation behavior may be multiply determined. For example, prior research has suggested that affective responses such as happiness ([32]) and guilt ([ 7]) may influence charitable behavior. We measured some affective responses in Studies 2 and 5 (see Web Appendix 5). In Study 5, while we found some evidence that greater perceived resources could also increase happiness, happiness did not directly mediate the effect of price promotions on donation behavior. Furthermore, guilt did not mediate the observed effect in our studies, and a guilt-based account would predict a stronger effect for purchases associated with guilt (e.g., budget overspending in Study 5; an indulgence purchase in the additional study)—but we found an attenuated effect in these scenarios. Theoretical ContributionsThis research makes several important theoretical contributions. First, while existing research has examined the effect of price promotions on firms' performances and consumers' purchasing behaviors, there is a gap in the literature regarding whether and how price promotions can have important social consequences. This research fills that gap. Specifically, this research shows that price promotions can boost consumers' perceived resources and thereby increase their donation behavior—a positive social consequence. Second, while promotions and donation behavior have been studied together in the cause-related marketing literature, our research is unique in its focus on donation behavior as a consequence of a promotion rather than as the promotion itself ([56]; [59]). Third, our research adds to the donation behavior literature by identifying price promotions as a novel situational factor that drives consumers' donation behavior. More broadly, this research answers the call for a greater understanding of when and why marketing activities can contribute to a better world by improving consumer and societal welfare. Practical ImplicationsThis research offers pertinent and actionable implications for charitable organizations. Specifically, our findings may help charitable organizations make three important decisions:Whom to target: Consumers who have participated in price promotions. Our research indicates that these consumers have a greater charitable tendency, and it should be easier to identify and target them than to reach out to potential donors on the basis of individual characteristics (e.g., sympathy, donation history).When to solicit donations: Immediately after consumers make purchases. A few years ago, a global movement named Giving Tuesday was initiated by New York City's 92nd Street Y and the United Nations Foundation in the post-Thanksgiving season. Our findings not only help explain the success of this Giving Tuesday phenomenon but also provide insights about the timing for government or international organizations to initiate charitable campaigns.How to increase the effectiveness of donation appeals: Our research indicates that charitable organizations should pair their donation appeals with promotions for necessities (vs. indulgences) that offer immediate discounts (vs. future rebates). Furthermore, the donation appeals should direct consumers' focus toward the money they saved (vs. spent) in the promotion. These are ecologically valid factors in the marketplace, and charitable organizations can take advantage of them to optimize their donation appeals.Furthermore, this research suggests that firms can use price promotions as great opportunities to collaborate with charitable organizations. For example, the outdoor brand Patagonia has made a commitment since 2016 to donate 100% of its profits from Black Friday to charities ([39]). Unfortunately, in traditional cause-related marketing practices, consumers might doubt a firm's prosocial motivation because the benefits for the charity are contingent on consumers' purchases from the firm ([18]; [24]). Our findings suggest that by soliciting donations after consumers complete their purchases, firms can cultivate a purer image of corporate social responsibility. This strategy was exemplified recently by Ralph Lauren, which partnered with the World Health Organization to fight the COVID-19 pandemic by soliciting donations from customers immediately after they submitted their orders on the store's official online shop. This collaborative strategy between firms and nonprofit organizations represents a win-win situation that can benefit both stakeholders and contribute to a better world. Limitations and Future Research DirectionsThis research also raises interesting directions for future studies. First, while this research focused on the positive impact of price promotions on monetary donation behaviors, it would be interesting to explore the generalizability of this effect to nonmonetary donation behaviors (e.g., volunteering). We found preliminary evidence for this in Study 1, but more studies are needed to establish a robust effect. Second, in addition to exploring different types of promotions, future research could examine whether different elements of a promotion also moderate the effect of price promotions on donation behavior. For example, it would be interesting to examine whether the percentage or the absolute size of the discount has a greater effect on consumers' perceived resources. Third, future research could investigate other boundary conditions, such as consumers' chronic resource availability or price consciousness. These research directions would further enrich our understanding of the social implications of price promotions and could offer relevant insights for firms and consumer welfare. " 19,Evolution of Consumption: A Psychological Ownership Framework," Technological innovations are creating new products, services, and markets that satisfy enduring consumer needs. These technological innovations create value for consumers and firms in many ways, but they also disrupt psychological ownership––the feeling that a thing is ""MINE."" The authors describe two key dimensions of this technology-driven evolution of consumption pertaining to psychological ownership: ( 1) replacing legal ownership of private goods with legal access rights to goods and services owned and used by others and ( 2) replacing ""solid"" material goods with ""liquid"" experiential goods. They propose that these consumption changes can have three effects on psychological ownership: they can threaten it, cause it to transfer to other targets, and create new opportunities to preserve it. These changes and their effects are organized in a framework and examined across three macro trends in marketing: ( 1) growth of the sharing economy, ( 2) digitization of goods and services, and ( 3) expansion of personal data. This psychological ownership framework generates future research opportunities and actionable marketing strategies for firms aiming to preserve the positive consequences of psychological ownership and navigate cases for which it is a liability.","Technological innovations are rapidly changing the consumption of goods and services. In modern capitalist societies, consumption is evolving from a model in which people legally own private material goods to access-based models in which people purchase temporary rights to use shared, experiential goods ([ 7]; [30]; [101]). Many urban consumers have replaced car ownership, once a symbol of independence and status, with car- and ride-sharing services that provide access to a vehicle or transportation when needed. Physical pictures occupying frames, wallets, and albums have been replaced with digital photographs; moreover, songs, books, movies, or magazines can be pulled down from the cloud at any time to suit a consumer's mood. Half the world population now buys, sells, generates, and consumes goods and information online through connected devices ([48]), generating vast quantities of personal data about their consumption patterns and private lives. The many benefits that these technological innovations and new business models offer to consumers––from convenience to lower economic cost to greater sustainability––makes legal ownership of many physical private goods undesirable and unnecessary ([79]). Consumers are not, however, simply exchanging the consumption of solid goods (i.e., enduring, ownership-based, and material) for liquid goods and services (i.e., ephemeral, access-based and dematerialized; [ 8]; [11]). We argue that relationships between consumers and their goods are changing.Aligned with a Marketing Science Institute priority (2018–2020) to examine how economic macro trends are influencing consumers, we examine how this technology-driven evolution in consumption affects consumer behavior. We focus on ways in which changing consumption patterns are threatening, transferring, and creating new opportunities to cultivate psychological ownership—the feeling that something is MINE ([43]). It is a psychological state that is distinct from legal ownership. In contrast to the benefits accrued through consumers' reduced legal ownership of goods (for reviews, see [ 8]; [30]; [69]; [101]), a commensurate reduction in psychological ownership should typically be detrimental to both consumers and firms.Psychological ownership is, in many ways, a valuable asset. It satisfies important consumer motives and has value-enhancing consequences. Within consumers, psychological ownership satisfies an effectance motive––a basic and chronic motive to have control and mastery over their environment, and motives to express their identity to others and themselves ([15]). Moreover, the feeling that a good is ""MINE"" enhances attitudes toward the good, strengthens attachments to the good, and increases its perceived economic value (for reviews, see [31]; [86]; [95]; [96]). Downstream consequences of value to firms include increased consumer demand for goods and services offered by the firm, willingness to pay for goods, word of mouth, and loyalty ([ 4]; [41]; [42]; [119]). Given these important consequences, we argue that preserving psychological ownership in the technology-driven evolution of consumption underway should be a priority for marketers and firm strategy.Our article starts with the proposal that technological innovations are changing consumption along two dimensions: ( 1) replacing legal ownership of private goods with legal access to goods and services owned and used by others and ( 2) replacing ""solid"" material goods with ""liquid"" experiential goods (for examples, see Figure 1). We theorize that important consequences for consumer behavior are determined by the way these changes affect psychological ownership for goods and services—that is, by threatening, transferring, or creating new opportunities to preserve it. We identify underlying mechanisms of each effect on psychological ownership as well as relevant concepts to guide thinking and responses. To illustrate the value of our framework, we discuss these ideas in the context of three relevant macro trends in marketing: ( 1) growth in the sharing economy, ( 2) digitization of goods and services, and the ( 3) expansion of personal data. For each trend, our framework offers new predictions, opportunities for future research, and recommended marketing actions. We then note important caveats—cases in which psychological ownership could be undesirable or a liability to consumers and firms. We conclude by outlining next steps for consumer and strategy research within the three trends that we discuss in depth, and beyond, to other areas and broader questions.Graph: Figure 1. Evolution of consumption: dimensions of change and examples.Notes: Consumption is evolving along two dimensions of change. Consumers are replacing legal ownership of goods with legal access to goods and replacing ""solid"" material goods with ""liquid"" experiential goods. Examples are sorted into quadrants; their location within a quadrant does not imply different values relative to others listed in that quadrant. Psychological OwnershipPsychological ownership occurs when one feels, subjectively speaking, that a thing is ""MINE."" It can be considered a form of emotional attachment between consumers and the goods and services they use ([107]). Antecedents of psychological ownership––perceived control, self-investment, and knowledge––do overlap with many of the property rights typically included in the ""bundle of rights"" provided by legal ownership of private goods ([85]). However, even though legal ownership may often precede psychological ownership, legal ownership of a good is not a requirement to feel psychological ownership for it ([100]). Consumers feel psychological ownership for ideas and goods to which they have no legal claim, such as theories and neighborhoods ([106]; [120]). At the same time, consumers feel little ownership for organizations and goods to which they do have legal claim, such as companies in which they hold stock and sports memorabilia they plan to sell ([72]; [98]). The Web Appendix provides a review of psychological ownership, including ( 1) motives and antecedents, ( 2) processes linking antecedents to outcomes, ( 3) consequences of psychological ownership, and ( 4) moderators and boundary conditions of these relationships.Psychological ownership has value-enhancing consequences, which stem from an association of a good with the self and/or categorization of the good as ""MINE."" Due to psychological ownership, traits associated with the self and positive self-associations are transferred to the good, increasing emotional attachment to the good and enhancing its perception and value ([14]; [45]; [125]). Explicit categorization of the good as ""MINE"" appears to reframe the reference point from which it is viewed, changing the evaluation of the good from something that could be gained to something that could be lost. Loss aversion and the heightened attention to positive features of the goods that accompany this reframing increase its value, making people more reluctant to exchange it for money or other goods (for reviews, see [31]; [85]; [86]). Even goods that have more negative than positive features, if consumers actively choose to acquire them, benefit from the value-enhancing effects of psychological ownership ([126]).Attachment between the self and good for which psychological ownership is felt parallels attachment between consumer and brand ([94]; [115]). As with an attachment between consumer and brand, psychological ownership for a good is positively associated with consumer demand, willingness to pay, customer satisfaction, relationships, word of mouth, and competitive resistance, as noted previously. Psychological ownership is thus a valuable asset for firms to preserve, capture, and redirect.In short, documented effects of psychological ownership show it to be generally value-enhancing for consumers and firms ([31]; [86]; [95]). Our perspective is consistent with this evidence. Our focus is thus on how to preserve the value inherent in psychological ownership for goods, services, and brands in the face of technological change. Of course, there are exceptional cases in which consumers and firms find psychological ownership undesirable. To date, demonstrations of its liabilities have been limited to extreme cases, as when a good is associated with a personal failure or a disgusting stimulus ([70]; [73]). Subsequently, we identify more common instances in which consumers and firms may benefit from a decline in psychological ownership, an area ripe for future research to explore. Evolution of ConsumptionWe propose that technological innovations are driving an evolution in consumption along two major dimensions. The first dimension of change is from a model of legal ownership, in which consumers purchase and consume their own private goods, to a model of legal access, in which consumers purchase temporary access rights to goods and services owned and used by others. The second dimension of change is from consuming solid material goods to liquid experiential goods. In this section, we unpack each change and how it affects psychological ownership. In general, we argue that the changes reduce psychological ownership and the value that accompanies it, but their effects are not uniformly negative. Table 1 identifies cases in which each change threatens psychological ownership; cases in which it transfers psychological ownership to other goods, groups, and brands; and cases in which changes in consumption patterns create new opportunities to preserve psychological ownership at prechange levels. Table 1 also includes recommended marketing actions to leverage each effect on psychological ownership, which are described in greater detail in the sections discussing the macro trends of the sharing economy, digitization, and personal data.GraphTable 1. Evolution of Consumption: A Psychological Ownership Framework. 1 Notes: ♦ = recommended marketing actions to manage psychological ownership threats, transfers, and opportunities. Change 1: Legal Ownership to Legal AccessIn traditional capitalist markets, consumption of a private good was typically bound to sole, legal ownership of it. New access-based business models, made possible by technology-mediated platforms, fracture this model. Whereas property rights are typically bundled in private ownership (e.g., use, modify, profit from, or transfer rights; [58]), fractional ownership models unbundle property rights, allowing consumers to acquire a right to temporarily use goods and services that are often shared with tens, hundreds, or thousands of consumers (e.g., by paying for or sharing personal data; [30]; [123]). These models are distinct from previous models of collective consumption within families and communities ([38]). They relinquish ownership rights to firms and strangers and shift the goal of collaborative consumption. In collectives and families, the goal is to help others and facilitate relationship building. In access-based models, the goal is typically to provide financial or efficiency gains for consumers and firms ([68]).Access-based models facilitate the creation of new products (e.g., social media platforms, video conferencing), and provide considerable benefits by changing the way existing products are consumed. By relinquishing private legal ownership of goods, access-based consumption offers consumers greater economic value, better preference matching, convenience gains from avoiding the entanglements of ownership (e.g., maintaining a car or vacation home), more sustainable means of consumption (e.g., digital books), and the use of both scarce and new goods that would otherwise be unaffordable or infeasible (e.g., luxury goods and social media platforms, respectively). The economic, temporal, and social benefits derived from the absence of legal ownership have been well documented (e.g., [ 8]; [57]; [69]; [101]). We argue that when access-based models induce a commensurate reduction in psychological ownership, however, there are negative downstream effects for consumers and firms. We briefly introduce how access-based consumption affects psychological ownership by threatening it, by causing it to be transferred, and by creating opportunities to preserve it.Access-based consumption models threaten psychological ownership in two ways (see Table 1). First, fractional ownership models of access-based consumption divide property rights across agents, who may each possess one or more of the legal rights to ( 1) use a good; ( 2) profit from its use or sale; ( 3) modify the form, substance, or location of the good; or ( 4) transfer possession of some or all of these rights between agents ([52]). This change impinges on perceived control over access-based goods, a critical antecedent of psychological ownership ([ 5]). Second, the impermanence associated with access-based goods also threatens psychological ownership ([ 8]). Psychological ownership often entails the expectation that one will possess a good in the future. This expectation shifts the reference point from which the good is evaluated, as something that is to be lost, rather than as a potential gain. When consumers expect goods to be returned or relinquished, however, they do not shift the reference point from which they evaluate the good. They are users who perceive the good like a ""buyer"" would, not as an ""owner"" would. Users view its consumption as a temporary gain in their happiness or utility, not as part of a new status quo that will be lost when they give back the good ([86]).Access-based models may also effectively transfer psychological ownership away from individual goods and toward consumer communities. Collective consumption of access-based goods may threaten psychological ownership for individual goods because they are used ([64]). They circulate among many consumers synchronously or asynchronously ([37]). Their circulation makes them interchangeable means to fulfill a goal. Therefore, consumers may use a good but not view it as ""MINE"" or unique or special ([80]). Their circulation also makes the symbolic meaning of access-based goods particularly vulnerable to contamination by dissociative social groups, persons, or acts ([60]). When consuming these used, circulating, or fungible goods, psychological ownership that would normally be directed toward an individual good (""It's MINE"") may be replaced by psychological ownership of the group of consumers who use it ([41]; [97]). Collective psychological ownership is a feeling that all consumers of a good or service share ownership of it (""It's OURS"") and gives each consumer a claim to membership, belonging, and ownership of the community formed ([97]).Finally, we see two opportunities for access-based consumption models to preserve psychological ownership at levels commensurate with the level observed for private goods. First, access-based consumption offers large assortments to consumers. More consumer choice could increase feelings of psychological ownership for goods and services through the greater control it provides to consumers ([59]; [87]). A second opportunity stems from the new channels for self-expression that access-based models provide. Self-expression is a fundamental motive driving the desire to own and consume ([15]), and access-based consumption facilitates this identity signaling ([17]). Access to more choices within and across product categories, and to new channels such as social media platforms, provides consumers means to more precisely signal authentic and desired identities as well as to accumulate social capital, attention, and future economic gain ([ 6]; [41]; [67]). Change 2: Material to ExperientialNew technologies are replacing ""solid"" material goods (i.e., tangible objects that are acquired and owned by consumers) with ""liquid"" experiential substitutes (i.e., events or experiences that one encounters and lives through) to fulfill a variety of hedonic and utilitarian wants and needs ([ 7]; [11]; [17]; [47]). This mirrors a shift in consumer demand, driven by millennials but also applicable to other generations, whereby consumers now prefer to spend money on experiences rather than things and have increased the share of their income spent on experiences ([ 9]). Beyond the multitude of new experiential offerings made possible through the expansion of the sharing economy, digitization, and an information economy driven by personal data (discussed subsequently in detail), firms are making significant investments in servitization and experiential offerings. Firms now offer a variety of product-focused services and experiences to consumers postpurchase. In many cases, even the acquisition of material goods is becoming refocused on its experiential components. Brick-and-mortar retailers, seeking differentiation from more convenient online platforms, for instance, have embraced ""experiential shopping"" (or ""shoppertainment"") with pop-up shops, live events, interactive displays, activities, product lessons, and interactions with experts ([44]).Many goods could be classified as material or experiential (e.g., a DVD is a tangible material object, but the film it plays is an intangible experience). Our classification scheme sorts goods according to the focal acquisition goal—to have a thing or an experience. A consumer could acquire an album with the goal to expand her record collection, or to listen to the music pressed into its vinyl form ([25]). Even traditional solid goods (e.g., cars, computers, phones, watches) are often now also sold with accompanying experiential features (e.g., applications such as GPS, music streaming, and games). We predict that eventually the material versus experiential distinction will be blurred to the extent that consumers will view most goods as experiential by default. Next, we briefly introduce how the change from material to experiential consumption affects psychological ownership by threatening it and causing it to be transferred, as well as how this change creates opportunities to preserve it.Two threats to psychological ownership arise from the substitution of material goods with experiential goods. The first is the intangibility of experiential goods. Psychological ownership is typically imbued through physical cues such as holding, touching, and manipulating a material object, which instantiate perceived control over it ([95]; [100]). This lack of physical interaction should consequently reduce psychological ownership for experiential goods—and, thus, their value—to consumers ([ 4]).A second threat to psychological ownership is the reduced evaluability of ownership––the difficulty evaluating who owns experiential goods, such as determining which property rights belong to consumers, owners, and intermediaries ([11]; [25]). When a consumer buys a concert ticket to a live event, what rights does that afford her other than access to the show? Can she be denied admission if she fails to comply with security and health protocols? Can she film it for personal consumption or share her recording on social media? Whether a consumer, intermediary, or firm ""owns"" an experience is often ambiguous, even when firms strive to make legal ownership transparent (e.g., who holds which property rights), and is muddled further when firms make legal ownership strategically opaque. Consumers who buy digital books, for instance, often mistakenly believe they have purchased more than the right to permanently view them ([56]).If consumers think of experiential goods at a higher categorization level than similar material goods (i.e., at a more abstract level), psychological ownership may transfer from individual goods (e.g., a book) to branded services, platforms (e.g., Audible), or technological devices used to consume them (e.g., a tablet). Vertical transfers may direct psychological ownership for material goods to brands of experiential goods or the platform through which experiential goods are accessed. Self–brand attachments may strengthen, and possession–self attachments may weaken, as experiential goods replace material goods ([32]; [39]). If psychological ownership manifests at the brand level, it can have positive downstream effects on consumer demand. Germans who felt more psychological ownership for a car-sharing service more frequently booked cars from that service, and students who felt more psychological ownership for a music streaming platform reported using it more often each week ([41]). Horizontal transfers may direct psychological ownership from material goods to the intermediary devices used to access experiential goods. Phones, computers, smart panels, watches, and other technological devices may accrue greater psychological ownership, value, and significance in the eyes of consumers (e.g., [81]).One opportunity to preserve psychological ownership at levels commensurate with feelings for material goods comes from consumer's greater self-identification with experiential than with material goods (e.g., a trip to Italy vs. an Italian jacket; [25]; [46]). We posit that the more positive social signal provided by experiential than by material purchases ([10]) may undergird their potent value as self-signals. Consumers may forge stronger attachments to experiential than material purchases, because they are more socially appropriate means with which to define the self. Three Marketing Macro Trends: Sharing, Digitization, and Personal DataAs evidence of the value of our psychological ownership framework, we present three macro trends in marketing disrupting existing business models, whose effects on consumer behavior are mediated by changes in psychological ownership: ( 1) growth in the sharing economy, ( 2) digitization of goods and services, and ( 3) expansion of personal data. We selected these trends because they are disrupting the marketplace and are active foci of interdisciplinary research. For each trend, following our framework, we identify specific threats to psychological ownership, transfers of psychological ownership to other stimuli, and opportunities to preserve psychological ownership at prechange levels. Marketing actions are then recommended to counter the threats and leverage transfers and opportunities. Exemplary case studies appear in Table 2 (ride sharing), Table 3 (digital music), and Table 4 (health and wellness), which concretely illustrate the explanatory power of our psychological ownership framework for scholars and practitioners.GraphTable 2. Case Study #1: Ride Sharing. 2 Notes: ♦ = recommended marketing actions to manage psychological ownership threats, transfers, and opportunities.GraphTable 3. Case Study #2: Digital Music. 3 Notes: ♦ = recommended marketing actions to manage psychological ownership threats, transfers, and opportunities.GraphTable 4. Case Study #3: Health and Wellness Data. 4 Notes: ♦ = recommended marketing actions to manage psychological ownership threats, transfers, and opportunities. Trend 1: The Sharing EconomySharing has traditionally been restricted to familiar others, such as family members and homogeneous collaborative or cooperative social groups ([68]). The new sharing economy is comprised of strangers, who together participate in ""a scalable socio-economic system that employs technology-enabled platforms that provide users with temporary access to tangible and intangible resources that may be crowdsourced"" ([30], p. 7). Its many forms of collaborative consumption include renting, reselling, lending, simultaneous consumption, and resource pooling ([20]). Sellers provide temporary usage rights for unused goods in exchange for profit. Buyers acquire access rights to those goods without worrying about outright purchase or upkeep. Thus, value is created for both parties ([35]; [68]). Sharing platforms lower matching costs between sellers and buyers, and secure the exchange of money, by strengthening trust through reputation systems ([ 7]; [29]; [112]).The staggering growth of products available and platforms for sharing, including bicycles, boats, cars, clothes, homes, offices, rides, and scooters (e.g., Airbnb, Bird, Blue Bikes, Lyft, Poshmark, Rent the Runway, Turo, Uber, WeWork) may threaten the long-term viability of private ownership. For instance, personal car ownership declines when sharing is a viable option ([83]), perhaps most for those who do not see car ownership as central to their identity ([18]). As an example, Table 2 illustrates how ride sharing threatens, transfers and creates opportunities to preserve psychological ownership. Legal Ownership to Legal Access Threats to psychological ownershipFractional ownership models prevalent in the sharing economy threaten psychological ownership, whether access-based goods are rented in exchange for payment or borrowed for free. Consumers report feeling less psychological ownership for rented goods than goods they privately own. This gulf is widened when goods are free. Consumers feel less psychological ownership for borrowed than rented goods. Indeed, they feel no more psychological ownership for borrowed goods than goods they merely evaluate ([ 5]). Marketing actions can be taken to counter threats posed by fractional ownership. First, marketers could emphasize the benefits of reduced costs and dependencies when forgoing legal ownership (e.g., avoiding car payments, gasoline, parking, cleaning, insurance, and general maintenance; [57]). Second, firms can recruit consumers as both users and suppliers, or ""prosumers"" ([30]; [102]). Seeing the transaction from the role of supplier should increase value by increasing consumers' attention to what is gained through fractional ownership ([86]).A second threat to psychological ownership from sharing markets is that consumers rightly expect their ownership rights and possession of goods to be temporary. Marketers could counter this threat by extending access to goods and services consumed in the present, or promising future access to those particular goods and services ([31]; [100]). A dress could be lent for longer, a ride-share platform could provide consumers with frequent access to their highest-rated vehicles and drivers, or a home rental service could give a consumer first claim to her favorite past rental on the same set of dates each year. Transfer of psychological ownershipIn the sharing economy, consumers interact with individual goods, but those goods are not the goal of consumption. The goods are fungible means to an end. Most consumers use a ride-share platform for transportation, for example, not to have the experience of riding in a particular car. The ensuing transfer of psychological ownership from individual goods to user communities can create a ""tragedy of the commons"" ([54]), whereby individual users take less care and responsibility for a shared good than they would if it were theirs alone. [ 7] note such negative reciprocity for car sharing. Contamination concerns may also loom large in the sharing economy. Consumers may be disgusted by sleeping in a bed in a rental property that has been slept in by many others, or worried about riding in a car previously used by a sick passenger.Multiple marketing actions can be implemented to preserve psychological ownership with such transfers. One marketing action to counter the lack of a unique relationship with any particular good may be to emphasize what is unique about the goods, such as their features, history, or owner ([49]; [71]). Second, beyond maintaining and advertising high standards for sanitation, background checks, and screening for irresponsible users, firms may use counterconditioning ([78]). Attractive, trustworthy brand ambassadors and clean and modern goods may counter the negative associations from dissociative groups and contamination concerns ([ 2]). Third, marketers could also try to retain psychological ownership at the group level, developing consumer communities around common geographic regions, interests, or goals (e.g., Uber Brooklyn; Uber Coachella; Uber Pool for work). Membership in such groups could reduce behaviors associated with reduced personal responsibility, such as obstructing sidewalks with electric scooters, and increase the attractiveness of sharing goods as a substitute for private goods ([41]). Opportunities to preserve psychological ownershipA shift from legal ownership to legal access also offers opportunities to preserve psychological ownership. More ride-sharing options enable users to better satisfy unique needs than car-buying consumers with one vehicle for all purposes (e.g., commuting, grocery shopping, travel). Decision aids may facilitate such preference matching. Soliciting the purpose of a trip or inferring it from locations (e.g., restaurants, airports), may allow a ride-sharing service to recommend suitable transportation options (e.g., a large SUV to carry luggage). Platform design can incorporate customization opportunities, such as choosing the brand of car or music in a ride share, the color of an outfit, or the towels and bath products in a home rental. Firms can also coordinate matches between customers and goods, such as when hotels configure mutable features of rooms to loyalty program member preferences (e.g., minibar, pillows). Psychographics should enable firms to target promotion-focused consumers willing to take risks with novel experiences and product categories, particularly as product trials are freed from the costs of long-term ownership.Another opportunity to preserve psychological ownership is via self-expression, expressing preferences and identities with goods that would otherwise be unaffordable or untenable to consumers. A student might rent a designer gown through a platform for a special occasion or social media post. A couple on a date night might treat themselves to a ride in a limousine, a car that would be impractical and onerous for them to privately own. Being able to use and broadcast use of aspirational and luxury goods through sharing platforms may produce greater identification with, psychological ownership for, and loyalty to brands accessible through the platform, which consumers may not normally buy. This includes goods used infrequently (e.g., formal attire, party supplies), that are costly to maintain (e.g., boats, vacation homes), or that are expensive to buy (e.g., handbags, yard equipment). Firms may further benefit from facilitating user posting of experiences on social media for social signaling and from soliciting user feedback. Vacationers may feel greater attachment to a rental after sharing pictures of it, or after expressing their values by writing a review of the home ([55]). Material to Experiential Threats to psychological ownershipIn the sharing economy, consumers may remain in physical contact with ""solid"" material goods, but the focal goal is not to own material goods. It is to consume goods in ""liquid"" experiential forms ([ 7]; [30]; [101]). A ride-share user purchases a ride, not a car. A vacationer purchases access to a home, not the home itself. A freelancer buys access to a workspace and its amenities, not the property on which she works. A first threat is raised by the intangibility of such experiential goods. This reduces physical control, and thus perceived control over the consumption experience. To offset this threat, marketers could use techniques that restore control through other dimensions, such as providing consumers with touchscreen interfaces (e.g., smartphones; [21]), or control over when and how goods will be consumed (e.g., scheduling rides and routes; [13]), the sensory features of the experience (e.g., temperature, music), and less tangible options (e.g., interactions with the driver or owner; [103]).Second, the rights afforded by the purchase of a shared good (e.g., a ride, rental of a vacation home) are more subjective and less evaluable than the rights afforded by private ownership of good (e.g., a car, a home; [11]; [25]). Consumers buy a contract for a ride from point A to point B, or to use a house for several nights, but which rights are included in that contract can be ambiguous. The end result is that consumers may not be able to discern (or feel) ownership of the experiential good they have purchased. To enhance the evaluability of owning shared experiential goods, marketers could cross-sell or bundle private material goods that serve as a marker of the experiential purchase. Tangible goods can serve as reminders of personal memories and meaningful consumption episodes ([121]). The French Laundry gives diners a branded wooden clothespin, for instance, as a souvenir of their extravagant meal. Such cues create value through the indexical connections they form, tangible links between consumers and meaningful events ([50]). Platforms could provide consumers with other cues such as usage history records or gamify use, such as by pinning maps with landmarks visited. Transfer of psychological ownershipPsychological ownership for the concrete, tangible, material goods used in the sharing economy may be transferred to the more abstract, intangible branded platforms and intermediary devices through which experiential goods are accessed. While this may reduce psychological ownership for any individual experience, positive effects of this transfer could include higher brand loyalty, competitive resistance, and word of mouth for brands and intermediary devices ([ 3]). We recommend that marketers emphasize the relationship with the platform in their strategy and actions. Consumers may care less about how the particular brands of cars available through a ride-share platform reflect on their identity, for instance, than the fairness of its prices or its treatment of drivers. Opportunities to preserve psychological ownershipThe sharing economy may afford particular opportunities to preserve psychological ownership. Consumers may more readily identify with collections of unusual experiences (e.g., renting a 1980s Mercedes convertible while vacationing in California) than with material merchandise that does not reflect their authentic selves (e.g., buying the same convertible to drive to work; [63]). A consumer can purchase experiences to signal that she is adventurous or on trend ([ 7]; [16]). Firms positioned toward identity marketing could target consumers who identify as ""minimalists,"" who prefer to avoid entanglement in the responsibilities of ownership ([57]). The appeal of using products collectively could be highlighted to appeal to consumers who identity with sustainable consumption, and firms could address their environmental concerns with premium sustainable offerings (e.g., electric cars, passive houses). Trend 2: DigitizationDigitization of goods and services, wherein information is converted into a numerical format, has evolved from niche scientific and commercial applications in the 1950s and 1960s into a technology that has spread across and transformed society. Consumers exhibit strong demand for digital goods. There has been a recent rise in consumer demand for some vintage physical goods such as vinyl records ([90]), but many analog products and services have been, or are being, replaced by digital substitutes. Digital cameras outsold analog camera sales by 2003. Both were outsold by smartphones in 2006, which were used to take most of the more than 1 trillion photographs taken in 2017 ([23]). By 2018, record labels earned more through streaming services than physical CD sales. Mass digitization of millions of books is currently underway by Google, the Open Content Alliance, and Microsoft ([27]). Digital currencies, from dollars to information-based currencies such as Bitcoin and Ethereum, may eventually replace cash.Digital goods provide similar consumption experiences as their physical counterparts, but their immateriality confers numerous advantages. A digital photograph can be shared instantly with friends and family members. It can be recovered even if the phone used to take it is lost or broken. Digital music and books can be purchased and accessed at home, on the beach, or in the air––anywhere with wireless access—from a pocket-sized device, never scratching, fading, or tearing. Digital goods have many environmental benefits, from lower carbon footprints to no waste on disposal ([82]). Effects of digitization on psychological ownership for goods, and its downstream consequences, are less clearly positive. As an example, Table 3 illustrates how digitization threatens, transfers, and creates opportunities to preserve psychological ownership of music. Legal Ownership to Legal AccessDigitization is replacing permanent ownership models with access-based consumption models in many domains ([30]; [123]). In the case of music, private ownership of physical albums is being replaced with access-based consumption of digital downloads and streamed music (Table 3). Streaming is now the most popular way to consume music. Diffusion of digital access-based models is also widespread for books, email, films, magazines, maps, news, and television. Threats to psychological ownershipAccess-based consumption of digital goods typically entails the temporary right to use a good, housed on a cloud server, which is owned and fractionated by a third-party provider. Consumers cannot sell, trade, or gift digital goods for which they purchased ""permanent"" access; they have only purchased a right to personally consume it. Consumers often do not even own digital consumption objects they create (e.g., annotated books, avatars in games, playlists). We suggest that this fractional model of ownership threatens the psychological ownership felt by owner-users, potentially transferring perceived ownership to the platforms and brands providing consumers access to digital goods. Indeed, consumers feel less psychological ownership and are thus less willing to pay for digital books, films, and photographs than their physical counterparts, ([ 4]; see also [108]). In addition, even though users spend more than an hour of their time each day on social media platforms each day, they are willing to forgo access to their content and online social networks for relatively small sums of money ([22]). Marketing actions for firms to address this threat could highlight the considerable economic and transactional benefits of access-based digital goods, which are often more attractive than the benefits of legally owning private goods ([109]).Second, consumers (rationally) view their ownership of access-based digital goods as impermanent. Streamed goods are often not even rented. Consumers pay for access to a platform's catalog, and individual goods are only possessed for the duration of their consumption. The ability to consume access-based digital goods—even goods that consumers themselves created—is typically determined by the platform on which they are hosted ([84]). Consumers may thus not feel ownership even for the digital goods they can ""permanently"" access. Indeed, consumers are willing to pay more to purchase than rent utilitarian physical goods (e.g., a hardcover textbook), but they are not willing to pay more to purchase than rent similar digital goods ([ 4]; [ 5]). We suggest that marketers respond to impermanence threats by assuring consumers that they will have continued access to the same digital goods. Platforms could extend streaming access to favorite titles in their catalog, or guarantee access to digital goods purchased ""permanently"" for a specified time period. When updating platform designs and formats, we conjecture that retaining elements that instill a perception of continuity may reduce this threat. Transfer of psychological ownershipIssues around transfer of psychological ownership due to the collective consumption of digital goods raise different concerns than those described in the sharing economy. Digitization should mitigate physical contamination of goods, but consumers may still be concerned about acquiring digital goods from dissociative groups, who may add malware or viruses. We speculate that contamination may also affect digital goods at higher construal levels. Whereas consumers may be primarily concerned with the previous owners of one copy of a physical good (e.g., ""This paperback of The Fountainhead was owned by a white nationalist""), consumers may be concerned with the previous and other owners of any copy of a digital good (e.g., ""The Fountainhead is popular on Facebook with white nationalists""). As contamination effects become more diffuse, however, they may also become more diluted. Contamination may be more potent when it applies to one rather than to all copies of a particular good. As digitization facilitates the coordination of social groups around collective activities and interests (e.g., games, music, news, photography, design, literature, videos), ownership for goods may be replaced with ownership for these consumer communities ([97]). Consumers may feel psychological ownership for the community itself as well as for their contributions that further the goals and formation of these groups (e.g., posts, comments, virtual objects).Marketing actions to retain psychological ownership for an individual digital good include providing consumers with more information about its background (e.g., history; critical reviews and summaries; information about individual artists, actors, or musicians involved in its production; [71]), and counterconditioning by featuring beloved artists, awards, or celebrity users in marketing communications for the good (e.g., social media influencer endorsements). Marketers who aim to benefit from the transfer could grow consumer communities by creating officially licensed clubs, posting content in spaces where consumers interact with each other and brands or artists (e.g., Facebook fan pages, Twitter), and providing consumers ways to engage with and invest their time and energy in digital objects and these social groups (e.g., hosting forums, posting reviews and comments, creating collaborative quests and interconnected worlds; [40]). That investment is likely to foster a feeling of psychological ownership for digital consumption objects (e.g., avatars, posts, virtual cities; [61]; [91]), which have considerable value for firms as means to lock in consumers to their platforms ([84]). Opportunities to preserve psychological ownershipDigitization provides opportunities to preserve psychological ownership through the panoply of options and channels for the self-expression it affords consumers. Digital goods enhance control and provide consumers with large assortments of content to match their preferences. Consumers typically can choose which digital media to consume anytime, anywhere, with even more choice on the go than when choosing similar kinds of physical goods at brick and mortar retailers (e.g., books, games, movies, music). Digital goods can also enhance control by facilitating the personalization of consumption experiences. The increased control imbued by enhanced consideration sets and customization may create a greater level of psychological ownership than is experienced for comparable physical goods ([59]; [87]). Marketing actions that can leverage these benefits include maintaining large choice sets, even as recommendation systems improve ([62]), offering consumers ways to customize their consumption experiences, and direct control over those experiences or the content offered (e.g., in games or media feeds). Low marginal costs and image filters for digital photographs, for instance, allow consumers to capture many images of the same subject and edit the photograph that best realizes their vision ([118]). As illustrated by the consumer backlash against Apple for adding U2's Songs of Innocence album to user libraries in 2014 ([12]), firms should avoid curating consumer content without their explicit consent.A second opportunity to preserve psychological ownership stems from the many new ways digital goods allow consumers to create and signal their identity to others through the cocreation of public digital consumption objects. Indeed, consumers invest considerable labor in creating and curating their image, content, and contacts on social media, in games, and in online virtual worlds ([84]). Marketing actions that facilitate these forms of self-branding and identity signaling would provide consumers with ways to share their preferences for and consumption of digital goods through social media and recommendation systems, and by including aspirational digital goods in their catalog of offerings (e.g., Pinterest walls, upvotes and downvotes, digital artifacts, new or exclusive content). Material to ExperientialDigitization, by definition, translates analog material media to an immaterial digital format that can be transmitted and consumed experientially through a variety of devices, including computers, smartphones, tablets, headphones, radios, and wearable devices. Digitization can also facilitate new material forms of consumption and exchange. For example, 3D printing may present consumers with new ways to buy, share and create material goods, based on digital plans acquired from business-to-customer or customer-to-customer markets, exchanges, or collaborations. Threats to psychological ownershipOne threat posed by this transformation is intangibility. The immateriality of digital goods imbues them with many remarkable benefits but prevents consumers from having physical interactions with digital goods ([21]; [95]; [100]). Consequently, consumers are less likely to establish a feeling of psychological ownership for digital goods, which leads them to value digital goods less than similar physical goods ([ 4]). Marketing actions to directly address this threat include interfaces that restore physical cues signaling control ([21]), allowing consumers to control the rate, time, and place at which digital goods are consumed ([13]) and positioning digital goods along sensory dimensions where they outshine physical analogues (e.g., [103]). Digital games allow consumers to navigate virtual worlds with joysticks, touchscreens, or their bodies (e.g., Xbox Kinect), for instance, to play at any time with people around the world and explore complex novel worlds. Online courses might benefit from haptic annotation tools, the ability to watch lectures at accelerated rates or asynchronously, the opportunity to save screenshots of slides and whiteboards, and novel animations that would be infeasible to incorporate in offline courses.A second threat to psychological ownership is reduced evaluability. It is often difficult to determine who owns experiential, digital goods ([92]). Consumers may incorrectly identify who owns the rights to share and transmit the goods, particularly in contexts where they are allowed to share physical goods. A consumer might see that it is illegal to sell a stranger access to her streaming account but will freely share access with roommates or family members. Beyond cross-selling and bundling physical goods with digital goods to create physical reminders of ownership (e.g., toys, clothing), digital goods may be able to serve as indexical reminders of meaningful memories by incorporating usage history features that identify when, where, and with whom they were consumed. Digital photographs, for instance, already include information about their date, location, and the people included in the photograph. Digital goods are ripe for gamification, whereby levels of ownership may be indicated by completion of real or arbitrary goals and status levels (e.g., pages read each week). Transfer of psychological ownershipDigital goods may lead consumers to transfer psychological ownership from the particular good being consumed (e.g., ""My LP"") to higher levels of categorization or abstract properties of the consumption experience, such as the genre, artist, recording, brand, or platform (e.g., ""I'm listening right now to Kind of Blue by Miles Davis on my Spotify playlist""). This could also lead consumers to feel greater ownership for the services and intermediary devices they use to consume digital goods, such as platforms and smartphones ([41]), as those touch points will be the primary means by which consumers control experiential goods ([13]). We suggest that digital goods are likely to be perceived more as services than goods. Consumers expect interactions with firms to entail the delivery of a consumption experience or experiences over time and to be an enduring relationship, rather than a fleeting transactional exchange (e.g., buying access to stream an evolving catalog of music vs. buying a vinyl album, respectively). Firms need to adapt their marketing strategy toward this service orientation in the minds of their consumers. Problems with digital goods, for example, are thus likely to be perceived as service failures, and strategies to maintain customer satisfaction may need to change. On the upside, servitization may then become a potential route through which to preserve psychological ownership at the brand level. Depending on the level at which psychological ownership manifests, brands may need to retain and develop consumer brand attachment through vertical integration or brand alliances that allow them to sell intermediary devices, which may become important means of self-expression (e.g., recognizable designs for smartphones, headphones, laptops). Opportunities to preserve psychological ownershipOne opportunity to preserve psychological ownership is that the experiential nature of digital goods may increase consumer identification. Identity marketing strategies, such as emphasizing associations or the fit between digital goods and salient consumer identities (e.g., trendiness or sustainability) may be particularly effective ([19]). Given their flexible categorization, if digital goods are marketed as experiences rather than as digital substitutes for material goods (e.g., as readings of books by their authors vs. as audio books), consumers may more strongly identify with their consumption and feel levels of psychological ownership comparable to that felt for their material substitutes. Trend 3: Expansion of Personal DataThe expansion in the recording of and analytics to manage and use personal data, defined as ""any information that relates to an identified or identifiable living individual"" ([33]), is fundamentally changing life and business, particularly how marketing is done for firms and experienced by consumers ([93]). Technological advances in collection, storage, and analysis as well as the transformative shift to online search, shopping, and fulfillment has both enabled and enhanced the value of firms using consumer data to power their marketing decisions. Consumers are realizing that their personal data have significant value ([76]). They want a share of that value as well as protection of their privacy ([99]). Regulatory bodies are dramatically increasing the legal ownership rights of consumers to their personal data by requiring consumers to ""opt in"" to permit firms to use/sell the data (e.g., General Data Protection Regulation, California Consumer Privacy Act; [28]). In early 2020, two U.S. states have passed and nine other states are in final stages of passing new consumer data regulations, where ""we're witnessing the beginning of a massive shift toward protection for consumer data and accountability for businesses that control and process it"" ([104], p. 1).The changing regulatory policies illuminate a tension between firms and consumers with regard to who owns the incredible breadth and depth of personal data. Firms try to capture as much data as possible on potential and existing customers to target the ""best"" consumers with the right products at the right time, increasing sales and profits. This data, once constrained to the history of a consumer at a single business, is increasingly associated with identity-relevant information about all facets of their lives (e.g., locations visited, photographs and videos, search history, medical and genetic information). In this context, firms would like to reduce consumers' psychological ownership of their personal data because this would promote consumer sharing their data with fewer restrictions or needs for compensation. As emerging firms (e.g., Datawallet, Midata) offer consumers opportunities to regain control of their personal data and sell it to firms, consumers may become more concerned with retaining ownership rights ([ 1]). Understanding these changes and identifying heterogeneous segments will be key to effective marketing strategies related to personal data and consumer privacy. As an example, Table 4 illustrates how the expansion of personal data threatens, transfers, and creates opportunities to preserve psychological ownership of health and wellness data. Legal Ownership to Legal AccessIn the past, consumers received and saved paper copies of their financial transactions, providing them physical ownership of these data. Now, consumers receive online access to platforms of financial intuitions providing cloud-based digital records of their personal financial data on as-needed basis. In government and business sectors, digitization is rapidly replacing physical documents with digital files from taxes to driving and medical records (e.g., [24]). Housing consumer data and giving consumer online access can result in switching barriers and consumer loyalty ([26]), but we argue that this model is changing consumer psychological ownership of their personal data. Threats to psychological ownershipFirst, access-based models are fractionalizing data ownership. Data is becoming more distributed, which could threaten consumers' psychological ownership of their data. Once private to consumers, data is now gathered and sold (or shared) by companies to third-party vendors (e.g., advertisers). The results of genetic testing were once accessible only to the consumer and her doctor. Firms such as 23andMe now offer consumers access rights to their genetic records, which are also shared (anonymously) with the parent company, other firms, and researchers. Tax records were once physical documents consumers prepared (perhaps with an accountant) and submitted to the government, keeping private physical copies stored in their files. Now taxes are prepared through intermediary platforms that keep a digital record, which the platforms use to market credit cards and loans back to their consumers. Even private copies of records stored by consumers in an electronic form may be accessible to cloud server hosts (e.g., Dropbox, Google). Location data, once exclusive to consumers, is now tracked by phone companies, government, GPS, and sold for profit (e.g., for mobile advertising).Initial technological and purchase trends associated with fractional ownership reduced consumer data privacy (social media, peer-to-peer payments, online shopping), but this is being offset by new technologies (blockchain, two-factor authentication) and regulations addressing data privacy concerns. Privacy and anonymity can be provided in exchanges by the use of cryptocurrency (e.g., Bitcoin), blockchain open source commuting platforms (e.g., Ethereum), or emerging decentralized autonomous organization, a complex form of smart contracts using token governance rules ([128]), which offer multiple research opportunities. Marketers may find that these technologies give consumers real and perceived control over their data, reducing threats to psychological ownership posed by fractional models of legal ownership.Second, the perceived impermanence of personal data threatens psychological ownership in situations where electronic access replaces permanent storage of a ""hard copy"" (e.g., lab reports, tax returns). As with digital goods, access to these data depends on the longevity and security of the hosting platform. When platforms hosting data close, or organizations change where their data is housed, data not transferred to new platforms may be lost. The frequency and scope of data breaches and ransomware attacks are additional salient reminders of the impermanence of personal data, even when firms prioritize privacy ([77]). Marketing actions providing consumers with the permanence necessary to preserve psychological ownership for their data may include long-term file storage, and continuity in file structures and platform interfaces. Providing real safeguards and privacy protections should be an effective marketing strategy to attract consumers with security-based psychological ownership concerns (e.g., Datawallet, DuckDuckGo, Midata). Transfer of psychological ownershipA change in the consumption of personal data and experiences may transfer psychological ownership from the individual to the collective space ([61]). Most consumer data were formerly consumed individually or among family members. Now, with the increased availability and consumption of metadata, social media, community forums, and other network-based apps, those data are now often consumed jointly or collectively. Power companies present the energy consumption of individual households and their neighbors side by side ([105]). Patients share information in online health forms about their health conditions with strangers ([113]), which may provide them with a feeling of membership in and ownership of a patient community. Workout classes at Orangetheory Fitness publicly display identifiable consumer heart rate data, in real time, on the same monitor with others in their class. The normative influence of social comparison and the emotional relief of sharing experiences can be powerfully motivating, but may replace psychological ownership of personal data with membership in the groups with which it is shared.Firms may increase collective psychological ownership for this data by soliciting consumer investment in its inputs; facilitating prosharing norms by asking consumers to share experiences, strategies, and ideas (e.g., medical symptoms and treatments; [111]); having consumers vote on goals for the community to pursue (e.g., how to reduce energy consumption); and helping consumers further the goals shared by the group (e.g., fundraising for members struggling to make their health care payments). Firms can present group-level data as a benchmark of progress toward collective goals, or to differentiate rival groups (e.g., competitions between neighborhoods in average household energy consumption). Platforms dependent on user-generated content may be particularly invested in such forms of community building, which are known to increase member contributions and usage ([111]). Opportunities to preserve psychological ownershipAccess-based models also afford potential opportunities to preserve psychological ownership. Consumers have more choice as they select and manage data inputs, outputs, and visualizations from medical tests and devices. These choices can be facilitated by increased data integration and personalization. Regulatory changes are also helpful in offering more choice in privacy options, such as via the ""right to be forgotten."" Customizable disclosure settings give consumers the ability to selectively remove their data from the collective space and increase their individual privacy ([34]). Fine-tuning desired disclosure levels across multiple platforms and audiences could increase perceived control of the data. To foster psychological ownership, developing and communicating policies that give the customer greater control and choice over which data is harvested or shared will be important, such as by providing consumers with an opt-out default as they trade access for personal data ([ 1]). Other means to preserve perceived control include enhancing consumer control over shared data with analysis tools for evaluating and displaying personal data shared with a firm.A second way to preserve psychological ownership of personal data is through the considerable opportunities for self-expression and social group membership afforded by publishing personal data. While the majority of users do not post personal information on social media ([111]), many consumers do divulge a variety of personal data online, such as their location on Foursquare or Instagram, their employment on Twitter or LinkedIn, their family on Facebook, and their spending on Yelp, Amazon, or Mint. Firms can facilitate new channels for positive social signaling—such as ways to express desirable knowledge, experience, or status—to increase data disclosure and consumer ownership. This strategy may work best with digital natives, extraverts, and narcissists, who are particularly likely to disclose personal information on social media platforms ([111]). Material to ExperientialThe expansion of the collection and use of personal data in business is recategorizing data that was once associated with material or physical records as experiential. Data that was ""static"" in the past, such as a physical report of heart rate and blood pressure measured once during an annual physical, are often now continuously collected and displayed in real time on wearable devices or through application dashboards with animation, audio, and gamification ([66]; [74]; see Table 5). Another emerging and potentially sensitive source of experiential personal data comes from the Internet of Things, as many home appliances (refrigerators, washers) and systems (electrical, HVAC, water) are continuously monitored and their output harvested, capturing activity about consumers' daily lives ([124]).GraphTable 5. Evolution of Consumption and Psychological Ownership: Open Questions. 5 Notes: PO = psychological ownership. Threats to psychological ownershipThese more experiential forms of data may threaten psychological ownership due to intangibility, more ambiguous evaluations of ownership, and the higher categorization level at which experiential data are construed. Consumers may feel less control over disclosure of intangible cloud-based continuous data than static physical records. Perceived control may be particularly impaired if firms remove actual user control by fixing the manner in which data is collected, accessed, and presented. A shift to experiential consumption of data, however, could increase psychological ownership of that data if firms give consumers more control of its disclosure, display, and delivery, facilitating identification with the data and its consumption (e.g., see their health data as an indicator of ""me"" rather than ""it""; [40]; [125]). Internet-enabled devices and wearables could give consumers the ability to ""mute"" data reporting. Platforms can facilitate the accessibility of data when consumers desire it. At any time of day or night, a patient may receive test results and request referrals from her primary physician on MyChart or initiate a prescription refill via SMS or IVR communication with her pharmacy. Psychological ownership could also be enhanced through haptic (e.g., touchscreen) interfaces and dashboards that control privacy settings (e.g., [21]).A second threat to psychological ownership that arises from the immateriality of data is reduced evaluability, meaning that it is difficult to determine who owns the data. A consumer might feel less psychological ownership for a dynamic heart rate report during a fitness class than for a printout reporting her static heart rate during a physical because ownership of the dynamic data is more ambiguous. It may belong to the consumer, the firm that manufactured the device on which it is recorded, the firm supporting the application on which it is displayed, or the firm running the cloud server where it is stored. In other cases, consumers may claim ownership for data that are not ""theirs."" When consumers use the internet to answer questions, for instance, they misattribute possession of that knowledge to themselves ([122]). Indexing or gamifying data to form a record of meaningful personal events (e.g., exercise classes, family birthdays, graduation), or making it a meaningful story in itself, such as achieving a health or wellness goal, may bolster consumer psychological ownership. Transfer of psychological ownershipA shift from more material to experiential forms of personal data may prompt a transfer in psychological ownership between categorization levels, from the individual data (e.g., my cholesterol level) to the applications and intermediary devices and platforms that provide access to that data (e.g., iHealth, iPhone, or MyChart, respectively). Consumers may feel considerable ownership of their accounts and devices. They may also hold platforms and firms rather than themselves responsible for security. Beyond providing consumers with opportunities to personalize their accounts and intermediary devices, firms should prioritize customer satisfaction and position brands and platforms in ways that allow consumers to feel psychological ownership for them (e.g., highlight identity consistency, emphasize the unique history of the company or platform, encourage consumer self-investment). Opportunities to preserve psychological ownershipA related opportunity to preserve psychological ownership for personal data as it shifts to more experiential forms is to capitalize on consumer identification with experiences. As data evolve from static documents to dynamic portraits of the self across time, data may provide a record of experiences that confirm important identities to consumers. A record of a run could be a social signal to potentially broadcast to others but could also reaffirm an important identity to a consumer (e.g., runner, athlete, fit). Identity marketing, whether integrated into data capture or display or positioning, could create feelings of ownership for these dynamic experiential records of consumers' lives. Liabilities Associated with Psychological OwnershipWe view psychological ownership as an asset that is typically valuable for consumers and firms to preserve ([41]; [86]), even in cases in which legal ownership is inconvenient or undesirable. Of course, there are caveats where consumers, firms, or both may benefit from its decline. We suggest four important cases for each. Liabilities for consumersConsumers may find psychological ownership to be undesirable ( 1) when it would amplify the pain of a sure loss, ( 2) when it would link them with identity-incongruent goods, ( 3) when it would increase the meaning of negative events or decrease the meaning of positive events, or ( 4) when a good will be shared. We discuss each of these points. First, when possession of goods is short term, consumers may wish to forgo psychological ownership to reduce the pain felt when returning goods, such as a rental car or dress, and thus avoid the strong feelings of loss felt when selling their car or donating their clothing ([116]). This avoidance is evident in the lack of psychological ownership felt by expert traders for goods they expect to sell ([72]) and by consumers of borrowed and rented goods ([ 4]; [ 5]).Second, because psychological ownership changes how consumers perceive not only the good but also themselves ([125]), they may avoid psychological ownership for goods that are identity incongruent. A cinephile may prefer to digitally stream a film before committing to the self-signal that buying it entails, for example, and pornography consumers may prefer to not feel psychological ownership for their browsing and search history.Third, consumers may eschew psychological ownership of goods that would increase the meaningfulness of negative events, such as a funeral or personal failure ([73]), and goods that would muddle other reminders of meaningful positive events (e.g., memorabilia from an unmemorable conference at a place where they vacationed with family; [127]).Fourth, consumers may try to avoid high levels of psychological ownership for goods that will be shared with others. Feeling greater psychological ownership for personal data could change consumers' personal comfort equilibrium with trading their data for free access to platforms that will sell it (e.g., Facebook), and prompt them to discontinue use of those desirable and ""free"" goods and services. Reduced psychological ownership should help reduce jealousy or territoriality when sharing physical goods ([65]). Psychological ownership for a good, and a more general attachment to goods ([36]), should thus be key predictors of engaging in the supply side of the sharing economy. For example, firms may find that a prospective homeowner who has yet to develop psychological ownership for a home ([89]; [110]) should be more comfortable with renting her home to strangers. Having decided to rent it, she might even purposely furnish it in a style that is discordant with her personal taste to establish a boundary between the properties in which she lives and lets. Liabilities for firmsWe identify four cases in which firms may benefit if consumers feel low levels of psychological ownership for goods, intermediaries, and brands: ( 1) when changes in access rights are likely, ( 2) when consumers are the product, ( 3) when it creates frictions in sharing markets, and ( 4) when service quality is inconsistent. First, like consumers, firms may prefer low levels of psychological ownership when access to goods is short-lived. When Microsoft ended sales of eBooks in April 2019, it deleted and refunded all books purchased through the platform. Consumers who felt stronger psychological ownership for the books in their digital library may have felt greater loss and anger when their access rights were revoked. More generally, for any digital goods or personal data, strong psychological ownership may breed resentment that access rights cannot be shared with or transferred to other consumers through sales, gifts, or inheritances.Second, many firms earn considerable profit from ""free"" services by mining and selling consumer personal data. In such cases, it may benefit firms to enact policies, contracts, and contexts that minimize psychological ownership of personal data (e.g., [ 1]). Consumers with high psychological ownership for their data may demand a share of profits or divulge less personal information ([76]).Third, if consumers feel high levels of psychological ownership for particular goods and brands, it may create frictions in matching consumer demand and supply, similar to market frictions in the endowment effect literature ([31]; [86]). A consumer with strong attachment to and psychological ownership for Mercedes cars, for instance, might be reluctant to book a car from a car-sharing platform if only Fords are available. Consumers who feel psychological ownership for a ""third place""––a social space other than at home or work, such as a seat in a café, bar, or park––may be more likely to visit it but will linger in that space ([51]). Firms may wish to keep psychological ownership low for access-based and experiential goods so that consumers are more receptive to a variety of goods and brands, or turn over quickly.Fourth, when dealing with consumers with high psychological ownership, firms will need to more carefully manage expectations and customer satisfaction ([117]). The value-enhancing effects of psychological ownership, if it has been transferred from the good to the brand, may heighten expectations and make firms more accountable for service failures in the eyes of consumers. If a ride-share car breaks down during a ride, for example, the consumer may hold the platform responsible rather than the driver or the automotive brand. Preserving psychological ownership may thus be a counterproductive exercise for platforms when service failures are likely. Future Research DirectionsApplying our psychological ownership framework and associated concepts to three macro trends in marketing identifies many opportunities for future research, some of which we previously outlined. Table 5 suggests additional opportunities for exploration. Psychological ownership is a central theme, but the list engages with a variety of major themes in marketing research. In consumer behavior, our framework informs research examining how technology is changing the self-concept, as well as critical relationships between consumers and technologies, goods, brands, and other consumers (e.g., [53]).Researchers focused on firm strategy and technological innovation will find that our framework delineates important considerations, boundaries, and opportunities for the acceptance and adoption of new consumption models and technologies. Many traditional brands have stumbled when entering access-based markets (e.g., car-sharing services such as BMW's ReachNow and GM's Maven) or when launching digital products (e.g., Barnes & Noble's Nook e-reader). Marketing strategists navigating the transformation from private material goods to access-based experiential goods cannot solely focus on and tout benefits of relinquishing legal ownership. Marketers should consider trade-offs between legal and psychological ownership as well as how to maintain the attachments, value, and loyalty to goods and brands that consumers derive from psychological ownership. Behavioral researchers need to identify the brands and sectors for which those attachments, value enhancements, and loyalties are most contingent on the preservation of psychological ownership (e.g., luxury goods). Firms and strategy researchers should test when product development, branding, and repositioning strategies preserve psychological ownership (e.g., servitization, vertical integration, brand alliances), which could be a lifeline for struggling industries and firms (e.g., retail, telecommunications, financial services). We have made many such suggestions throughout this article.The threats and opportunities to preserve psychological ownership identified by our framework generalize beyond the three macro trends in marketing we explore here to many technology-driven trends reshaping modern economies and life. Psychological ownership may affect consumer motivations for sustainable consumer behavior. It could increase preservation of shared resources, as it does for private goods. It could also be counterproductive and increase the consumption of those resources, if consumers anticipate others using them. Remote work and the move from live personal interactions toward virtual interactions is an area experiencing growth, accelerated by the COVID-19 pandemic. If remote work is the future of employment, how will virtual interactions affect psychological ownership among the parties involved? Will employees who work from home feel more or less psychological ownership for their ideas, projects, and firms, as compared to a live office environment? Will students feel less psychological ownership for online courses and degrees received for remote learning? Automation and artificial intelligence in both firm and residential applications is another such trend. Psychological ownership has numerous direct applications to its intersections with retailing and labor. Consumers may feel less psychological ownership and attachment to items chosen or purchased by or with the help of a recommendation system if using recommendation systems feels like relinquishing choice to another agent. The desirability of psychological ownership may then be an important factor in determining for which product categories recommendation systems, touchscreens, and voice interfaces should be integrated as decision aids or replace live salespeople. More generally, whether consumers feel psychological ownership for intelligent devices may depend critically on their positioning (e.g., tool vs. intelligent agent).Although we have suggested that transfer can occur, an important question remains regarding what happens to the aggregate level of psychological ownership felt by a consumer in response to these changes. When a consumer relinquishes a traditional good, does the aggregate level of psychological ownership she experiences also decline? Psychological ownership once felt for her amassed library of books, movies, and photographs, for instance, could decrease as it is digitized or transferred to devices and streaming platforms. Indeed, if psychological ownership is bundled into devices or platforms, diminishing marginal utility suggests that it will decline in the aggregate ([114]). However, psychological ownership satisfies core motivational drivers, so consumers may instead strive to maintain a set level of aggregate psychological ownership for their various attachments. They may then transfer the psychological ownership lost for one good to other targets (e.g., goods, devices, platforms). Our article focuses on changes to psychological ownership felt for individual goods, but how technology-driven consumption changes affect the aggregate level of psychological ownership consumers experience is a question critical for understanding the ebbs and flows of psychological ownership.Finally, we do not address heterogeneity in the experience of psychological ownership, but it is likely that features of psychological ownership are not universal or static. They are manifested differently across cultures as well as within cultures with different forms of economic transaction. Psychological ownership does not appear to generate the same degree of value enhancement for East Asians or descendants of East Asian cultures, for instance, as it does for White Americans or people descended from European cultures ([75]). Generational differences may affect how psychological ownership is affected by the macro trends we have identified. Digital natives who have grown up with music streaming and targeted mobile advertising may be less threatened. Firms need guidance to develop and deploy effective targeting and positioning strategies across cultures, generations, and other groups. ConclusionTechnological innovations are changing consumption models from permanent legal ownership of private physical goods to access-based use of temporary, experiential, and collective goods. Consumers benefit from forgoing legal ownership of goods in these fractional ownership models (e.g., money, time, effort; [ 8]; [69]). However, giving up legal ownership does not imply that psychological ownership, a generally desirable source of value for both firms and consumers, must or should also be relinquished.We illustrate the worth of a psychological ownership framework for anticipating and understanding consumer responses to this technology-driven evolution in consumption. Our framework predicts when technological innovations will threaten, transfer, and create opportunities to preserve this valuable asset, and it identifies accompanying research opportunities for marketing scholars. We have mapped our framework to three key macro trends: ( 1) growth in the sharing economy, ( 2) digitization of goods and services, and ( 3) the expansion of personal data. For each trend, we offer recommendations for how managers can counter threats to psychological ownership and leverage opportunities to preserve or enhance it through a variety of strategies. We also note cases in which consumers and firms benefit from letting psychological ownership decline. More broadly, our framework applies to many sectors where technology is changing consumption, and it is informative for managers vying to attract and retain customers within these new environments. It outlines many ways in which psychological ownership will continue to be a valuable lens through which to view, understand, forecast, and manage the consumer experience. " 20,Examining Why and When Market Share Drives Firm Profit," Many firms use market share to set marketing goals and monitor performance. Recent meta-analytic research reveals the average economic impact of market share performance and identifies some factors affecting its value. However, empirical understanding of why any market share–profit relationship exists and varies is limited. The authors simultaneously examine the three primary theoretical mechanisms linking firm market share with profit. On average, they find that most of the variance in market share's positive effect on firm profit is explained by market power and quality signaling, with little support for operating efficiency as a mechanism. They find a similar explanatory role of the three mechanisms in conditions where market share negatively predicts profit (for niche firms and those ""buying"" market share). Using these mechanism insights, the authors show that the value of market share differs in predictable ways between firms and across industries, providing new understanding of when managers may usefully set market share goals. The authors also provide new insights into how market share should be measured for goal setting and performance monitoring. They show that revenue market share is a predictor of firm profit while unit market share is not, and that relative measures of revenue market share can provide greater predictive power.","Many firms use market share to set goals and monitor marketing performance, and market share is also widely used in research examining marketing's performance impact ([24]; [40]). [20] recent meta-analytic study (hereinafter, E-H 2018) reports a significant positive relationship between a firm's market share and its economic performance and identifies contingencies affecting this relationship. However, while the literature suggests several reasons market share may drive firm performance, few empirical studies have directly examined any (and none more than one) of these mechanisms. Thus, little is known about the underlying ""why"" of mechanism(s) linking firms' market share and economic performance and how they may both explain previously identified moderators and facilitate identification of additional moderators of this important relationship. In addition, when understanding of the mechanisms linking market share with firm performance suggests that it is economically valuable to measure market share for goal setting and performance monitoring purposes, managers currently have no empirical insights into how to do so.These knowledge gaps are important because understanding why market share is linked to firms' future profit can provide new insights into when and where market share is most likely to be valuable. While many firms use market share as a marketing performance metric, our research identifies new ways for managers to assess when this is most appropriate—and when it may not be. Because market share is such a common marketing goal, this is also important in delineating the role that marketing plays in determining firm performance and in understanding contingencies that may affect this role. Exploring the predictive value of alternative measures of market share, we also provide important new insights into how market share goals should be set and performance assessed via different market share measurement options in terms of unit versus revenue market share and absolute versus relative market share.In addressing these key questions, this study offers several contributions. First, we provide the first direct empirical assessment of the three primary causal mechanisms that have been theorized to link market share with firm profit: market power, operating efficiency, and quality signaling. Using direct measures, we examine each of these three mechanisms simultaneously and show that both market power and quality signaling are key mechanisms linking market share with firm profit. On average, we find little evidence of theorized economies of scale and learning benefits of market share, but we identify conditions under which such efficiency benefits do exist. We find no support for a fourth theorized mechanism linking market share negatively with profit as a result of a strong competitor orientation. However, we do find support for the same three mechanisms in conditions under which the market share–firm profit relationship is negative—for niche firms and when a firm ""buys"" market share. Overall, these findings provide important new empirical insights into market share's value-creating role.Second, using these new causal mechanism insights, we explore the consistency of the market share–profit relationship across different types of marketplaces and firms where the relative value of market share via the three mechanisms may be expected to vary. We show that the market share–profit relationship varies across industries and firms, and that the different causal mechanisms identified provide high explanatory power for such variations; thus, all three theories from which the hypothesized mechanisms arise can be ""correct."" In addition, this insight provides an empirically supported way for managers to identify when setting market share goals and monitoring market share performance may be more or less valuable. In contrast, we find that using indirect contingencies to try to infer the mechanisms linking market share with performance relationship often does not align with the directly observed mechanism effects, further indicating the value of direct measures in understanding the ""why"" mechanisms involved.Third, we extend recent meta-analytic insights regarding the nature of the relationship between market share and firms' economic performance by using direct measures of the three most widely cited mechanisms: measures of both revenue and unit market share and different market share benchmarks, firm size controls to isolate the benefits of market share versus firm scale, and different econometric approaches to address panel data and endogeneity estimation concerns. These aspects of our study enable us to provide several new insights. For example, we show that for most firms, economies of scale arise from firm size and not firm market share. They also allow us to identify which market share metrics are most predictive of profit for different types of firms and the economic value of increasing market share on these metrics. This is useful new knowledge for managers because it provides new insights into how market share should be measured in goal setting and performance monitoring as well as the scale of profit benefits that may be expected from any gain in a firm's market share.The article is organized as follows. First, we develop a conceptual framework and hypothesize relationships involving the three key mechanisms by which market share may be linked with firms' future profit. Next, we use the three mechanisms to identify three conditions under which the market share–profit relationship may be expected to be stronger versus weaker. We then describe the data set assembled and analysis approaches used to test the hypotheses and discuss the results. Having shown that the three mechanisms collectively mediate the market share–profit relationship, we then assess whether this remains true even under conditions when the market share–profit relationship is negative. Next, having shown that managers can use knowledge of the three mechanisms to identify when market share is likely to be economically valuable for their firm, we assess how managers may best measure market share. Finally, we assess the implications of our study for theory and practice and identify new questions for future research suggested by our findings. Conceptual Framework and HypothesesMuch of the theorizing regarding market share and firm performance in economics and management concerns related but distinct phenomena such as firm size and market concentration. We focus only on relationships that directly pertain to firm market share and the mechanisms underlying its economic value. As a result, we center our market share conceptualization on revenue market share—units sold × realized price (i.e., sales revenue) divided by total market sales revenue. In doing so, we conceptualize and measure the ""market"" as comprising firms selling similar product/service offerings. However, we also examine unit market share—units sold divided by total market unit sales—as well as several different operationalizations of revenue market share in robustness checks and post hoc analyses. Market Share and Firm Economic PerformanceThe marketing literature generally views market share as an indicator of the success of a firm's efforts to compete in a product-marketplace (e.g., [13]; [63]). From this perspective, market share is an outcome of a firm's marketing efforts including its advertising and promotion, product/service offering quality and price, channel and customer relationships, and selling activities ([24]). All of these are evaluated relative to those of other suppliers by customers (channel members and end users) when they consider and select offerings, which is what conceptually distinguishes a firm's market share (how the firm's sales compare with those of the total market) from its sales revenue (the number of units sold × price). Importantly, this means that (unlike sales revenue) market share is not a component variable in any indicators of firm economic performance,[ 6] so there is no synthetic (or ""hard-wired"") market share–firm economic performance relationship.Historically, the empirical literature provided conflicting and equivocal answers concerning the ""main effect"" relationship between firms' market share and their economic performance (e.g., [11]; [36]; [37]). However, the recent E-H (2018) meta-analysis using more sophisticated methodological approaches has provided new insight on this question, showing a generally positive effect of market share on firm economic performance. We corroborate this in our data and focus our hypothesizing on why this relationship exists and how this ""why"" understanding may help explain and predict differences in the strength of the relationship across firms and industries. Mechanisms Through Which Market Share May Impact ProfitWhile several explanations have been independently proffered for why a firm with higher market share may enjoy superior economic performance, three mechanisms are much more widely discussed than others. As Figure 1 shows, we focus our theorizing on these mechanisms and consider how each may link a firm's market share with its profit.Graph: Figure 1. Conceptual framework. Market powerThe first proposed mechanism by which market share may be linked with firm profit is via market power (i.e., the firm's ability to influence the price of its product/service offerings by exercising control over demand, supply, or both; e.g., [10]; [59]). Industrial organization theory posits that firms enjoy superior profit when they are able to charge higher prices than rivals, which is determined by the availability of alternatives to customers and firms' ability to create and/or control resources that give them stronger market positions (e.g., [57]). Market share may be a resource that provides a firm with the opportunity for greater market power over both ""upstream"" suppliers and ""downstream"" channels and customers and thereby control prices in several ways.For upstream suppliers, buyer firms with higher end-user market share are more attractive, which may allow them to negotiate lower prices and/or higher-quality inputs from their suppliers ([ 9]). For example, Apple's smartphone market share allows it to both charge app developers for selling their products and enforce strict quality controls on the apps it sells. It may also increase supplier willingness to cooperate with others in the buyer's supply network to further lower the buyer's input costs and improve input quality ([28]). For downstream channels, higher–market share firms are more attractive upstream partners because they generate end-user demand for more and/or higher-value products. They may also attract larger customer numbers and/or more frequent interactions for channels to engage in cross-selling. This may enable higher–market share firms to negotiate better list prices than rivals in downstream channels and to benefit from greater channel cooperation (e.g., preferred shelf-space, merchandizing support). For example, PepsiCo's snacks division leverages its leading market share position to obtain preferential shelf and display access in many U.S. retail chains. The input and go-to-market cost and quality benefits of higher–market share firms should allow them to provide better value offerings, which may thus allow them to charge higher prices to end users (as in the case with Apple) and/or enjoy higher profit margins on each unit sold (e.g., Walmart). Thus, H1: The positive effect of market share on firm profit is mediated by the firm's market power. Operating efficiencyThe second theorized mechanism by which a firm's market share may lead to profit is via increasing the firm's operating efficiency (e.g., [17]). Disputing market power arguments, the ""Chicago school"" in economics argues that market share is an outcome of firm efficiency that allows a firm to sell quality-equivalent offerings at lower prices than rivals, attracting greater demand (e.g., [14]; [50]). Following this logic, strategic management scholars propose that higher market share may also allow firms to further increase their efficiency in a recursive relationship with lowering firm costs via learning effects (e.g., [ 1]; [29]). Much of this logic is framed in terms of a firm's position on the production ""experience curve"" as a function of the volume of units sold, with greater experience allowing production-related learning and lower production costs (e.g., [30]). Thus, firms selling a greater number of units produce more and learn how to do so more efficiently. For example, Tesla has used its greater accumulated experience in producing electric vehicles (EVs) to lower its costs compared with rivals.Conceptually, this may also be possible via market share impacting the number of interactions a firm has with suppliers, channels, and customers, enhancing opportunities for higher–market share firms to learn and use knowledge gained to improve their supply-and-demand chains ([55]). For example, Tesla has used its greater EV sales to learn how to drive improvements in battery designs and configurations from suppliers as well as to optimize its own software to increase EV range. More interactions also increase the likelihood that suppliers, channels, and customers will trust higher–market share firms, increasing information sharing, lowering coordination costs, and enhancing cooperation in changes designed to enhance the firm's supply-and-demand chains ([16]; [27]). This should enable higher–market share firms to lower costs and enhance supply-and-demand chain quality and reliability, allowing superior value offerings for customers and/or greater margins. Thus, H2: The positive effect of market share on firm profit is mediated by the firm's operating efficiency. Quality signalingThe third mechanism by which market share may enhance firm profit is by signaling unobserved quality. Information economics theory posits that customers' limited evaluative knowledge often makes it difficult for them to observe ""true"" product/service quality (e.g., [38]; [41]). Empirical studies also show that customers are often unable to accurately (or confidently) evaluate an offering's quality prior to making purchase decisions, and they frequently rely on indirect cues (e.g., [47]; [61]). Market share may signal quality by increasing the credibility of firm claims and thereby lowering customer perceived risk ([21]; [34]). Customers may also infer that ""everyone can't be wrong"" in choosing the offerings of a high–market share firm (e.g., [18]). For example, Toyota campaigns have touted that its products are ""#1 for a Reason."" Thus, to the extent that market share signals higher quality, it should increase future demand and reduce customer churn. It may also lower the firm's costs relative to rivals, because alternative ways to signal quality (e.g., advertising) may be more costly.Market share may also signal quality to suppliers and channel members. Firms that are perceived to be producing high-quality offerings may be viewed by suppliers as not just attractive buyers, in terms of their own demand, but also as potentially providing a halo image spillover benefit. Similar to customers viewing them as having ""too much to lose"" to provide inferior offerings, supplier choices made by high–market share firms may be viewed as being based on ensuring high quality and reliable inputs to protect their reputation and market position. For example, Apple's suppliers are frequently identified as such in business press reports. This could also apply to channel partners where selling offerings that are perceived as higher quality can provide a halo effect making the channel member more attractive to other suppliers and end-user customers (e.g., [42]). All of these arguments suggest the following: H3: The positive effect of market share on firm profit is mediated by the firm's perceived quality. Using Mechanism Insights to Predict Where Market Share Is ValuablePrior research suggests that the value of market share varies across industries (e.g., [ 4]), indicating that setting market share goals may be more beneficial for some firms than others. To explore this, E-H's (2018) meta-analysis examines the sample characteristics most commonly reported in prior studies and reports that market share is more valuable in business-to-customer (B2C) markets and in markets with medium market concentration, whereas it is less valuable in the banking industry. While offering initial useful insight to managers, these boundary conditions are limited in number and scope—and the ""why"" mechanisms involved are unobserved. Robust empirical understanding of the mechanisms using direct assessments should allow additional boundary conditions to be identified and provide empirically verified principles for managers to distinguish when they should and should not care about market share.To provide an initial assessment of the predictive value of our mechanism results and offer new insights for managers, we next examine the extent to which the market share–profit relationship varies under conditions in which each of the three mechanism in turn may be expected a priori to be more versus less important. For each mechanism, we identify a condition expected to be particularly impactful on that particular market share–profit pathway. However, in our analyses we also allow for the possibility that each of the conditions we identify may affect the strength of all three mechanisms linking market share with profit. First, in terms of market power we examine industries characterized by higher customer switching costs, where firms are more easily able to retain customers. Firms should benefit more from the market power provided by market share when switching costs are high because they are better placed to increase prices without fear of customers switching ([23]; [58]; [60]).Second, in terms of the value of operating efficiency in explaining the market share–profit relationship, the literature suggests that cost-reducing learning effects are more likely earlier in the life of a firm (e.g., [66]). For example, ""experience effect"" studies of the value of a firm's cumulative doubling of output show that this is more likely to occur early in a firm's existence (e.g., [31]). In addition, learning effects require changing and adapting firms' processes—which tend to become more rigid over time (e.g., [54]). Thus, younger firms are less knowledgeable in their operations and less ""set in their ways,"" providing incentives to seek out the learning opportunities presented by market share and the ability to exploit the efficiency-enhancing knowledge gained via process changes.Third, to explore conditions where the quality-signaling value of market share may be stronger, we examine differences between ""service-dominant"" and ""goods-dominant"" industries.[ 7] A key difference between these markets is the greater intangibility of service offerings, which creates more quality uncertainty for customers ([68]). Under such conditions, customers are more likely to use cues such as market share as indicators of the quality of a firm's offerings (e.g., [12]). Interestingly, this prediction is the opposite of E-H (2018), who reason that physical goods manufacturers may benefit more from efficiency, and that this may be more important in driving profit than any dampening of the quality-signaling effect of market share in physical goods-focused markets. We explore this reasoning empirically when we directly examine the three mechanisms underpinning the market share–profit relationship.We therefore hypothesize the following: H4: The effect of market share on firm profit via market power is stronger in marketplaces with higher switching costs. H5: The effect of market share on firm profit via efficiency is stronger for younger firms. H6: The effect of market share on firm profit via perceived quality is stronger for firms selling service- versus product-dominant offerings. Methodology DataWe combine secondary data from a variety of sources. From Compustat, we obtained data to construct measures of market power and operating efficiency, firm economic performance indicators, firm-specific controls, and a set of industry and competitive context control variables. Equitrend provided data on the perceived quality of firms' offerings. To calculate measures of unit market share, we use unit sales data from the Global Market Information Database (GMID). We assembled our initial data set by merging data from Compustat and GMID. To test the mediation hypotheses, we also require data from Equitrend, for which our access covers only the years 2000–2013. Because each data source has distinct firm and year coverage, the compiled data set used to confirm the main effect of market share on firm profit and test the hypothesized mediation effects contains 3,058 firm-year observations from 244 individual firms, operating in 126 North American Industry Classification System (NAICS) four-digit industries, 2000 through 2013. The average firm in this sample has $13.81 billion in assets and has been operating for 45 years. Table 1 shows summary statistics and correlations for the main variables in our sample and additional details are contained in Web Appendix 1. To test H4–H6, we also required American Customer Satisfaction Index (ACSI) data (to measure switching costs), which reduced our sample for testing these three hypotheses to 2,629 firm-year observations from 207 firms (2000–2013).[ 8]GraphTable 1. Descriptive Statistics and Correlations (N = 3,058). 1 Notes: All descriptive statistics are for the ""raw"" (i.e., untransformed) variables. Correlations with an absolute value larger than.046 are significant at p < .01, and those greater than.035 are significant at p < .05. Hypothesis Testing Variable MeasurementThe Appendix contains definitions and operationalization details of all variables described next. Market shareMarket share is the percentage of a market's total sales garnered by a firm over a specified time period ([25]). The market may consist of all suppliers selling products/services with the same characteristics, or those that are thought of similarly by customers and are purchased for the same use. We follow [35] to compute a measure of market share using a set of competitors and market definitions derived from business descriptions in firm 10-Ks. This allows market definitions to be dynamic, where a firm may move in and out of any given market depending on whether its offerings changed over time and thus compete with a different set of firms.To compute market share, we divide the total sales of each firm by the aggregate sales for that market for that year, where the market is dynamically defined as described previously using data from all 22,076 firms in Compustat for the 2000–2013 period. In defining markets, we note that each firm has a similarity/competition score with respect to any other firm (i.e., all possible dualities are computed) in the Compustat database. In line with [35], the number of competitors can be defined using a threshold of similarity scores and/or specified number of nearest neighbors (e.g., 50 or 20). We combine the two approaches and specify 50 as the largest number of neighbors, while also imposing a minimum threshold limit. Thus, our market definition comprises a maximum of 50 firms per industry, while allowing for fewer firms, to maintain a minimum level of similarity among competitors in the same market.[ 9]To assess the robustness of the findings using this dynamic measure of market share, we also use a more static approach, defining markets via each firm's primary NAICS designation using the four-digit level that researchers suggest most closely represents the real ""competed"" market (e.g., [44]). To calculate this, we first collect the total revenue-by-industry data that comprise gross domestic product (i.e., total expenditures on products and services) for all four-digit NAICS industries from the U.S. Bureau of Economic Analysis, which allows us to account for the sales of firms that are private, small, or otherwise not available in Compustat. We then divide the total sales of each firm by the gross domestic product value for that four-digit NAICS industry for that year. Firm market shares are computed from their revenues in their primary NAICS markets. Firm profitWe use net income as our primary measure of firm profit, obtained from Compustat. We use this indicator of absolute firm profit (while controlling for asset size in our model) because economic theories of the value of market share assume that maximizing the amount of profit—not the efficiency with which profit is generated, which is what ""return on asset"" (or investment) relative profit measures capture—is a firm's superordinate performance objective. Market powerWe use profit elasticity relative to the industry average (similar to [ 8]) to indicate firm-level market power. This is calculated by estimating regressions of firms' profit (net income) on their total variable costs for each industry as follows: ln(πit)=α+βln(tvcit)+εit, Graphwhere π is firm profit and tvc is the firm's total variable cost (Cost of Goods Sold + Selling, General and Administrative Expenses) for firm i at time t. Both profit and variable costs are scaled by firm size (total assets). Because profit and costs are natural log transformed, the β from this regression captures the average profit elasticity within the industry, with less negative βs indicating the average ability of firms within the industry to mark up prices when costs rise and thus exercise market power (e.g., [39]). Firm-specific residuals measure each firm's margins relative to its industry's average, providing an indicator of firm's market power ([ 8]). Positive residuals (equivalent to less negative elasticities) indicate greater market power, and negative residuals (i.e., more negative elasticities) indicate weaker market power. Web Appendix 2a indicates favorable face validity for this measure. Firm efficiencyFrom an economic theory viewpoint, this concerns producing goods and services in ways that optimize the combination of inputs to produce maximum output at the minimum cost ([ 5]). To operationalize productive (in)efficiency, we use a stochastic frontier estimation approach. Following [ 5], we use operating expense as the input and total sales as the output. In stochastic frontier estimation, the firm in the industry with the lowest input requirements to produce a given set of outputs forms the efficiency frontier and the closeness of a firm's inputs-to-outputs to this frontier determines its relative (to the industry's most efficient firm) efficiency. Web Appendix 2b provides evidence of strong face validity for this measure. Perceived qualityWe use the perceived quality measure of brands from the Equitrend database, which comprises consumer ratings on an 11-point perceived quality scale. For multibrand firms, we take the mean perceived quality of all brands owned by the firm.[10] Face validity assessments for this measure (see Web Appendix 2c) provide strong support for the measure. Switching costsWe use ACSI data and follow [53] to construct an industry-level measure of switching costs as the ""excess loyalty"" displayed by customers to suppliers using the residual of regressing each industry's customers' loyalty onto its customers' satisfaction, controlling for time fixed effects (FEs). This measure has been shown to have strong face validity ([53]), and we also find evidence of this (Web Appendix 3). Service- (vs. product-) dominant industriesService- (vs. product-) dominant industries is a dummy variable identifying firms operating in nonbanking (banks have idiosyncratic characteristics we later explore) service-focused industries using Fama–French industry definitions ([22]). Firm ageFirm age is the number of years since the firm's founding using information from annual reports and websites. Control variablesIn addition to firm and year FEs used to control for unobserved heterogeneity, we employ several firm- and industry-level covariates in our analyses, including firm size, operationalized as the logarithm of each firm's total assets to account for scale economies not captured by market share, and the firm's advertising and research-and-development (R&D) expenditures to control for firm-level heterogeneity. We also control for market growth that may affect the profit outcomes of market share ([56]), captured as the year-to-year change in total market sales.The Appendix and Web Appendix 1 summarize descriptive statistics for all variables used in our analyses. To enable log-log specification and interpretation in our analyses and reduce deviations from normality present in several of our measures (market share, firm profit, market power, firm efficiency, perceived quality, advertising expense, R&D expense, and market growth), we applied log transformations to our data.[11] Model SpecificationWe empirically test the hypothesized relationships using a fixed-effects autoregressive (FE-AR) estimation approach ([65]) for several reasons. First, we are using panel data, and the Hausman test indicates that an FE correction is needed to address unobserved heterogeneity and separate between time-variant and -invariant firm-specific errors. Second, several of our measures are longitudinally persistent, raising concerns about serial correlation—the AR correction of the errors addresses any potential bias to the estimates. The modified Durbin–Watson and Baltagi–Wu LBI tests indicate that an AR1 correction is appropriate. In addition, we control for heteroskedasticity using cluster-adjusted robust standard errors at the firm level. Finally, we estimate our hypothesis-testing models using generalized least squares (GLS), because OLS are statistically inefficient and may result in biased inference in the presence of serially correlated residuals.We first verify the average positive relationship between market share and profit (E-H 2018) and estimate the total effect using the following model specification: Profiti,t+1=α0+α1MarketSharei,t+α8FirmSizei,t+α9Advertisingi,t+α10R&Di,t+α11MarketGrowthi,t+YearFEs+ζi+εi,t+1, Graph( 1)where i stands for firm and t for time (year), ζi is a time-invariant firm FE, and εi, t + 1 is the random error representing all unobserved influences on future profit, modeled as an AR1 process such that εi, t + 1 = ρεi, t + ηi, t + 1 and where |ρ|<1 and ηi, t + 1 is an independent and identically distributed (i.i.d) error. Market Share, Firm Size, Advertising, R&D, and Market Growth are as described previously, and Year FEs are mutually exclusive year dummies. Lagged regressors are used to alleviate concerns due to simultaneity and reverse causality (i.e., future profit should not impact past market share).Having selected an appropriate estimation approach given the nature of our data, we next deal with potential endogeneity concerns with respect to omitted variables—of which reverse causality and simultaneity are special cases ([65]). We examine the potential for the presence and effect of such endogeneity concerns using a Gaussian copula correction to Equation 1 and assess the presence and effect of any endogeneity (including potential selection bias introduced by the various data sets on which we draw for our measures) via a likelihood ratio test of whether there is a significant difference between the uncorrected set of parameter estimates and the endogeneity-corrected set ([65]).[12] Once we show that potential endogeneity issues are not material, we empirically test H1–H3 using an identical FE-AR approach by estimating the following equations: Profiti,t+1=α0+α1MarketSharei,t+α2MarketPoweri,t+α3FirmEfficiencyi,t+α4PerceivedQualityi,t+α5SwitchingCostsi,t+α6ServicesDummyi,t+α7FirmAgei,t+α8FirmSizei,t+α9Advertisingi,t+α10RDi,t+α11MarketGrowthi,t+YearFEs+ζi+εi,t+1, Graph(2\rm a) MarketPoweri,t+1=β0+β1MarketSharei,t+β5SwitchingCostsi,t+β6ServicesDummyi,t+β7FirmAgei,t+β8FirmSizei,t+β9Advertisingi,t+β10RDi,t+β11MarketGrowthi,t+YearFEs+τi+ξi,t+1, Graph(2\rm b) FirmEfficiencyi,t+1=γ0+γ1MarketSharei,t+γ5SwitchingCostsi,t+γ6ServicesDummyi,t+γ7FirmAgei,t+γ8FirmSizei,t+γ9Advertisingi,t+γ10RDi,t+γ11MarketGrowthi,t+YearFEs+μi+ςi,t+1, Graph(2\rm c) PerceivedQualityi,t+1=θ0+θ1MarketSharei,t+θ5SwitchingCostsi,t+θ6ServicesDummyi,t+θ7FirmAgei,t+θ8FirmSizei,t+θ9Advertisingi,t+θ10RDi,t+θ11MarketGrowthi,t+YearFEs+νi+φi,t+1, Graph(2\rm d)where Market Power, Firm Efficiency, Perceived Quality, Switching Costs, Services Dummy, and Firm Age are as described in the variable measurement section, and all other variables and subscripts follow Equation 1. Finally, we empirically test H4–H6 by estimating the moderated-mediation contingencies and include interactions between Market Sharei,t and Switching Costsi,t, Services Dummyi,t, and Firm Agei,t in Equations 2a–2d. To estimate the relative effects of the three hypothesized mediation mechanisms (market power, firm efficiency, and quality signaling) and three moderated-mediation contingencies (switching costs, firm age, and services), we follow [51] using [64] approach to augment the FE-AR estimation. Results and Discussion Main Effect of Market Share on Firm ProfitPrior to testing the hypothesized mechanisms, we first verify the main effect results indicated in the E-H (2018) meta-analysis in our sample using several variants of the model specification detailed in Equation 1. We begin by estimating a model with FEs and cluster-adjusted robust standard errors that includes only the covariates as predictors (M1), to which we then add market share (M2), allowing us to verify the main effect of market share on firm profit and reveal its incremental predictive power. We also estimate this same model using an FE-AR error correction and cluster-adjusted robust standard errors (M3) to demonstrate the stability of the estimates across the different statistical corrections proposed. In M4 we examine whether the reported estimates suffer from endogeneity bias by including a Gaussian copula for the Market Share variable as a control function to empirically correct endogeneity bias. The likelihood ratio test for joint parameter differences ([65]) indicates that the endogeneity-corrected estimates in M4 are not statistically different from those in M3.As Table 2 shows, the estimates are consistent across all four models, demonstrating the robustness of the effect of market share on firm profit. In addition, while the Gaussian copula estimate in M4 is significant (.048, p < .05) indicating the presence of some omitted variable endogeneity, the likelihood ratio test indicates no significant difference in the market share parameter estimates between M3 (β = .137) and M4 (β = .159). This supports the use of an FE-AR( 1) (i.e., model specification M3) estimation approach and confirms that any remaining bias is modest and does not substantively impact the estimates. In a robustness check, we also replaced the dynamic market share measure with a four-digit NAICS alternative and again confirmed the main effect (Web Appendix 5). Finally, we further verified that endogeneity bias does not unduly influence our findings using a difference-in-differences version of Equation 1 comparing the market share–profit relationship for firms in industries that experience an exogenous demand shock (exit of bankrupt firms) with those that do not. The results (Web Appendix 6) again confirm the main effect findings.GraphTable 2. Main Effect of Market Share on Firm Profit. 2 *p < .05.3 **p < .01.4 ***p < .001.5 Notes: All model specifications estimated using 3,058 firm-year observations. M1/M2: GLS estimation, FEs and cluster-adjusted robust standard errors. M3/M4/M5: GLS estimation, FEs with AR errors and cluster-adjusted robust standard errors. Z-test difference in share coefficients between M3 (.137) and M4 (.159) = .64 (p > .05).Collectively, these analyses verify the main effect results in E-H (2018) that, on average, firm market share positively predicts future firm profit—and the effect sizes reported on Table 2 are both consistent and aligned with the average elasticity of.132 reported by E-H (2018), further enhancing confidence in our findings. Table 2 results also show the suitability of the FE-AR error correction and cluster-adjusted robust standard errors GLS estimation approach (model specification M3), which we employ in the hypothesis-testing analyses. Hypothesized Mechanism (Mediator) ResultsAs Table 3 shows, in testing H1–H3 we find support for both market power in M1a (.230, p < .001) and quality signaling (.141, p < .05) in M1c as mechanisms linking market share with firm profit. However, while M2 confirms that firm efficiency predicts firm profit (.129, p < .001), M1b reveals that a firm's efficiency is not predicted by its market share (.024, p > .1). Thus, on average we find no evidence supporting efficiency as a mechanism linking firm market share and profit in our sample. Overall, these results provide support for H1 and H3 but not for H2. As M2 shows, all three of the mechanism variables are significant predictors of firm profit, and the main effect of market share becomes insignificant (.031, p > .10) in the presence of these three variables. To examine the relative strength of the mediator role played by the three mechanism variables in explaining the market share–profit relationship, we follow [64] approach. This reveals that the three mechanisms collectively explain 77.37% of the total effect of market share on firm profit, with 63.21% of this flowing through market power, 33.96% via perceived quality, and 2.83% through firm efficiency.GraphTable 3. Mechanism for Market Share Effect on Firm Profit. 6 *p < .05.7 **p < .01.8 ***p < .001.9 Notes: 3,058 firm-year observations covering 244 firms for the 2000–2013 period (Equitrend available 2000–2013). Total effect (from Table 2: M3).137 (100.00%) minus direct effect (from M1a).031 (22.63%) = indirect effect of.106 (77.37%). Indirect effect via ( 1) Power = .067 (63.21%); ( 2) Quality = .036 (33.96%); and ( 3) Efficiency = .003 (2.83%).To check the robustness of the mechanism results, we conducted four additional analyses. First, to check for any potential scale effect of absolute sales revenue beyond firm size, we reestimated our model using market share ranks and adding firm sales revenue as a separate control. The estimates replicated the hypothesis-testing results (Web Appendix 7). Second, to check for any potential biasing effect of firm orientation to market share ([43]) we used text analysis of 10-K reports to construct an annual measure of each firm's market share focus based on the number of times ""market share"" is mentioned relative to the total number of words. When this is added to our model, we find that the results remain essentially unchanged (Web Appendix 8). Third, to ensure that results are robust to alternative firm performance measures, we replaced net profit in turn with return on assets and Tobin's q as dependent variables. As shown in Web Appendices 9 and 10, we replicate the hypothesis-testing results. Fourth, we also checked that a firm's competitor orientation—a potential fourth mechanism linking market share (negatively) with firm profit ([ 3])—does not explain additional variance in the market share–profit relationship. Using 10-K reports and [ 7] text-based measure, we computed the competitor orientation of each firm in our sample and included this in our model. As Web Appendix 11 shows, we find that while competitor orientation predicts firm market share, it does not materially affect the market share–profit relationship. Hypothesized Moderating Condition ResultsHaving demonstrated the robustness of the hypothesized mechanism results, we next examine whether the market share–profit relationship may be stronger in industry and firm conditions in which each of the three mechanism variables in turn may be expected a priori to be more versus less important as captured in H4–H6. The results are summarized in Table 4, with M1 showing that firms in industries with higher customer switching costs are more profitable (.137, p < .05), and M2 supporting H4 by confirming that market share is more valuable in such industries (.087, p < .001) via its stronger effect on market power (.157, p < .05). In addition, M4c reveals that firms also gain stronger perceived quality benefits from market share in industries with higher switching costs (.203, p < .05), suggesting that some of the switching costs we observe are due to customers continuing to choose a provider because of positive relational bonds that may influence both customers and others' perceptions of the quality of such firms' offerings.GraphTable 4. Main Effect and Mechanisms for Market Share Effect on Firm Profit in Hypothesized Moderators. 10 *p < .05.11 **p < .01.12 ***p < .001.13 Notes: 2,629 firm-year observations covering 207 firms for the 2000–2013 period (sample size due to ACSI data availability).The interactions reported for M2 also show that market share is generally less valuable for older firms (−.069, p < .001), and consistent with H5, the mechanism estimates in M4b provide strong evidence supporting the expected effect of market share on firm efficiency being weaker for older firms (−.109, p < .001). This is aligned with our rationale that efficiency-enhancing learning effects associated with market share accrue mainly to firms that are earlier in their development. M4c estimates also reveal that older firms benefit less from market share via quality signaling (−.092, p < .05). We reason that older firms that have been in the marketplace for longer are likely to be better known and also that firm age may indicate a firm's stability and lower risk, which reduce the signaling value of its market share.In terms of services-dominant firms, the significant positive estimate in M2 for the services × market share interaction (.056, p < .001) indicates that service firms benefit more from market share. However, our mechanism estimates in M4c show that this is not a result of the expected strengthening of the quality-signaling benefit of market share (.012, p > .10) posited in H6 but rather, as shown in M4b, that service firms benefit more from the efficiency-enhancing effect of market share (.148, p < .001).[13] Because controlling for scale effects via firm size isolates the efficiency-enhancing learning effects of market share, this finding suggests that market share provides a greater opportunity for service firms to learn how to operate more efficiently and to use this knowledge to change their operations to do so. We reason that this may be because the greater direct customer interactions from higher market share are more valuable in helping service firms learn how to efficiently deal with customer heterogeneity, and that applying what is learned may also be less capital-intensive for service firms (vs. manufacturers). Additional Analyses of Hypothesis-Testing EffectsTo provide additional insight into how the hypothesized moderators affect the profit value of market share via the three mechanisms, we examined these effects in an additional analysis (Table 5). Of the.086 total effect (elasticity) of market share on profit when the moderator variables are included in the model,.056 is indirect (65% of the total) via the three mechanisms, with 62% of this flowing through market power, 6% through firm efficiency, and 32% via perceived quality. Consistent with the H4 testing results (Table 4), the effect of market share on firm profit is strengthened by switching costs, with the total effect amplified by.287 for each unit increase in switching costs, of which.195 is indirect via market power (50.9%), firm efficiency (2.5%), and perceived quality (46.6%). These direct and indirect effects of switching costs on market share's effect on firm profit are proportionately lower (higher) at lower (higher) levels of switching costs (i.e., ± one standard deviation around average switching costs) with the indirect effects flowing through the three mechanisms in very similar percentages.GraphTable 5. Indirect Effects for Market Share Effect on Firm Profit in Hypothesized Moderators. 14 *p < .05.15 **p < .01.16 ***p < .001.17 Notes: 2,629 firm-year observations covering 207 firms for the 2000–2013 period (sample size due to ACSI data availability).Consistent with H5 testing results (Table 4), the total effect of market share on firm profit is also amplified for service-dominant firms by an extra.032, of which.012 is indirect (38% of the total) and flows through market power (41.0%), firm efficiency (21.0%), and perceived quality (38.0%). Meanwhile, for product-dominant firms, the total effect is reduced by −.032, of which −.022 is indirect, with 54.0% flowing through market power, 3.0% through firm efficiency, and the remaining 43.0% via perceived quality.Finally, in line with H6 testing results (Table 4), Table 5 shows the effect of market share on profit is weakened by firm age with each additional year reducing the total effect of market share on profit by −.136, of which −.122 is indirect (90% of the total) and flows through market power (12.1%), firm efficiency (45.5%), and perceived quality (42.4%). As we expected, the total effect of firm age on the market share–profit relationship is more pronounced for very high (old) versus very low (young) age levels, with a marked increase in the indirect effect flowing through firm efficiency (from 40.2% to 56.8%) and decrease in that flowing through market power (17.1% to 2.7%) in the case of very young firms. This is consistent with our Table 4 hypothesis testing results revealing stronger efficiency gains with market share for younger firms. Market Share–Profit Mechanisms When Market Share Negatively Impacts Firm ProfitAligned with E-H's (2018) finding that 82% of market share–performance elasticities in prior research are positive (82% of the same elasticities in our sample are also positive), our hypotheses are framed in terms of a net positive performance effect of market share. However, conceptual arguments concerning potential negative outcomes of market share have also been proposed (e.g., E-H 2018; [34]). Drawing on our theorizing, we expect that the three mechanisms we identify should empirically capture any negative and positive effects of market share. For example, any associated diseconomies of scale will reduce a firm's efficiency while a reduction in perceived exclusivity will affect the quality-signaling value of market share. To empirically verify this expectation, we identify two conditions under which market share's positive benefits may be outweighed by negative consequences, such that larger market share might reduce firm profit and reestimate the mediation effects of the market power, firm efficiency, and quality-signaling mechanism in these conditions. Niche firmsOne condition in which market share may negatively predict profit concerns firms with a strategic focus on serving a smaller segment of a market, usually a group of customers with a distinct set of needs and requirements (e.g., [49]). For example, Louboutin specializes in high-fashion stiletto shoes. By serving distinctive needs, niche-focused firms make money by occupying positions in a segment of a broader market in which competition is more limited (e.g., [19]). As a result, they may not serve enough customers to gain market power benefits from market share, and their specialist positioning may diminish any quality-signaling benefit. They are also unlikely to gain from any learning effects in production. However, niche-focused firms with higher overall market shares are likely to have achieved this by selling to customers beyond their original niche ([62]). This may negatively impact the firm's profitability by reducing its original niche appeal via a negative effect on perceived quality (e.g., [34]) and also attract more competition (e.g., [32]). These downsides may outweigh any potential market power and/or firm efficiency benefits of having a larger market share. Firms buying market shareAnother circumstance when market share may negatively impact profit is when firms ""buy"" market share by lowering prices relative to rivals. This is analogous to findings in the sales promotion literature that price promotions often produce negative returns (e.g., [33]). In this circumstance, any market share gain via greater market power and the ability to charge higher prices is not only relinquished but reversed. In addition, because there is a price-perceived quality heuristic among customers in many markets (e.g., [52]), charging lower prices may offset any quality-signaling benefit of higher market share, and the net result on perceived quality could be negative. Our previous results suggest that in most circumstances, these negative market power and quality-signaling effects are likely to outweigh any firm efficiency gains via learning produced by increasing market share. Empirical test of the two conditionsTo assess the robustness of our mechanism results under conditions when the market share–profit relationship may be negative, we first identified firms that are likely pursuing a niche strategy by combining a new text measure indicator of the degree to which a firm has a niche strategic emphasis (for details, see Web Appendices 4a and 4b) with the number of brands they market (both firms with both a high niche-focus in their product-market coverage strategy and those that offer only a single brand are likely to be niche firms). The face validity assessments in Web Appendices 4a and 4b support this identification logic. Second, to identify firms that may be ""buying"" market share, we created a dummy variable indicator for firm-years in which a firm both reduced its average prices (computed using GMID data) and experienced a positive market share change.We then reestimated our market share–profit models from Table 3 with the addition of the new niche firm measure and buying share dummy indicator, along with their respective interactions with market share. As Table 6 shows, model M1 shows that higher market share reduces profit for niche firms (−.115, p < .05). As we expected, M2c reveals that this is a result of a strong negative effect of market share via perceived quality (−.062, p < .001). M1 also shows that the effect of market share on firm profit is significantly lower for firms ""buying"" market share (−.036, p < .001).[14] The mechanism results indicate that this is caused by a significant reversal in both the market power (M2a: −.047, p < .001) and firm efficiency (M2b: −.033, p < .001) effects of market share and a reduction of the perceived quality mechanism to insignificance (M2c: −.022, p > .1). These findings suggest that any supplier input cost benefits of greater market power from market share are more than offset by lowering downstream prices to ""buy"" the market share. In addition, consistent with the well-known ""bullwhip"" effect, rapid increases in short-term demand resulting from lowering price seems to disrupt the efficient production and delivery of these firms' products and services. Overall, the Table 6 results provide support for the robustness of the three mechanism variables in mediating the relationship between firm market share and profit, even in the relatively rare conditions under which the relationship is negative.GraphTable 6. Moderating Effect and Mechanism When We Include Conditions in Which Market Share May Have a Negative Effect on Profit. 18 *p < .05.19 **p < .01.20 ***p < .001.21 Notes: 2,629 firm-year observations covering 207 firms for the 2000–2013 period (sample size due to ACSI data availability). For Niche Firms, indirect effect = 58%, of which Power = 21%; Efficiency = 0%; and Quality = 79%. For Firms Buying Share, Indirect Effect = 33%, of which Power = 56%, Efficiency = 22%, and Quality = 22%. Comparison with E-H's (2018) Indirect Moderator InferencesHaving provided robust evidence to support the three mechanisms, to offer additional insight on the utility of the direct measures of the three mechanisms employed, we also examined how the results compare with previous indirect inferences regarding these mechanisms drawn from observable moderators of the market share–profit relationship. To accomplish this, we first replicated E-H's (2018) measures as well as main effect and substantive moderator results (banking services, concentration, and B2C). We then examined the mechanisms explaining the effect of these moderators of the market share–profit relationship in our sample, and the results are revealing (Web Appendix 12). For example, we find that while E-H's theorizing focuses on quality signaling, the reason for the stronger market share–profit relationship in B2C industries is a significant strengthening of all three mechanisms relative to business-to-business (B2B) industries (market power:.143, p < .001; efficiency:.044, p < .05; quality:.082, p < .05). In addition, we find that while banks are in general more profitable (.426, p < .01) and have greater market power (.042, p < .05), this is in spite of—not due to—their market share (−.087, p < .05). In fact, results reveal that market share reduces banks' profitability by lowering their efficiency (−.410, p < .001). We also find a direct moderating effect for concentration (.109, p < .05), whereas E-H found a nonlinear effect, and we observe that this is via increasing the market power benefit of market share (.110, p < .01). These results show that using moderators to indirectly infer the three mechanisms underlying the market share–profit relationship often does not do a good job of isolating these mechanisms. This reinforces the value of direct empirical understanding of the mechanisms linking market share with firm profit in predicting when market share is more valuable and thus when managers should set market share goals. When Its Value Is Indicated, How Should Managers Measure Market Share?The new empirical understanding of the mechanisms linking market share with firm profit revealed in our analyses can help managers evaluate when market share may be a valuable goal. When its value is indicated, a manager's next task is to decide how to measure market share for goal setting and performance monitoring. To provide insights on this question, we examined two key market share measure design choices facing managers. First, ""share of what?,"" in terms of unit sales volume or sales revenue, should be used in computing market share ([ 6]). Managers use both types of indicators to track market share, and both rank among the most popular measures of marketing performance in practice (e.g., https://marketbusinessnews.com/financial-glossary/market-share/). The second is ""relative to what?,"" in terms of whether and how the firm's market share is benchmarked—as an absolute value (% of total market sales) or relative to others in the market (the market share leader or the top three players). Revenue versus unit shareTo provide insights on the first question, we replicated model M3 in Table 2 and replaced the sales revenue market share with unit sales volume market share using the same dynamic market definition. As we show in Table 7, in contrast to revenue market share (M2:.151, p < .05), unit market share (M1:.009, p > .1) does not predict firm profit. This result is robust to all of the same checks performed on our revenue market share main effect testing analyses and also to using benchmarked (vs. absolute) values of unit market share. Post hoc analysis of the mechanisms associated with unit share (Web Appendix 13) reveal that although it has a small positive effect on both market power and firm efficiency (consistent with the learning effect logic that market share is a proxy for number of units produced), this is insufficient to overcome the significant negative relationship with quality signaling. We reason that the weaker effect of unit (vs. revenue) market share on market power is a result of unit market share ignoring prices charged to customers (a downstream indicator of market power). The negative quality-signaling effect of unit market share is consistent with both ignoring price (which is often a quality cue for customers) and the notion that ubiquity reduces perceived exclusivity (e.g., [34]). These results show that when the presence of the three mechanisms indicates market share's value, managers should set market share goals and monitor performance in terms of revenue market share.GraphTable 7. Market Share–Profit Relationship Using Alternative Market Share Measures and Benchmarks. 22 *p < .05.23 **p < .01.24 ***p < .001.25 Notes: 3,058 firm-year observations covering 244 firms for the 2000–2013 period, except for model specification M1, which is estimated using 2,214 firm-year observations covering 235 firms for the period 2004–2013 (due to GMID data availability). In a subsequent robustness check, model specifications M2 through M4 were reestimated using the same 2,214 firm-year observations, and estimates remain identical. Absolute versus relative shareIn terms of the ""relative to what?"" question, in Table 7 we compared the market share–profit estimates of the absolute value of market share used in the main effect testing (M2) and two different relative market share benchmark operationalizations: relative to the market share leader (M3) and relative to the combined market share of the top three market share firms (M4).[15] The results indicate that benchmarked measures of firm market share provide stronger predictive power (of future profit) (M3:.222, p < .001; M4:.392, p < .001, respectively) than using absolute market share (M2:.151, p < .05). Subsequent analysis of the three mechanisms show that this is a result of the relative market share measures ""dialing up"" the market share–market power link (Web Appendix 14). This is likely due to such ""relative to others in the same industry"" measures capturing some of the industry-level market concentration power that our previous analysis showed increased the market share–market power relationship in terms of both switching costs (which are higher when markets have fewer equivalent players) and average market share (as an indicator of market concentration in the E-H [2018] replication analyses). Implications Implications for TheoryThis study offers several new insights into theories of firm behavior and performance. First, economic theory assumes that market share predicts firm profit but offers different reasons for why this relationship exists. We provide the first simultaneous test of three mechanisms proffered in competing economic theories for this relationship and show that in combination, they explain the vast majority of the variance in the market share–profit relationship. This suggests that individual single-theory lens explanations of the mechanisms linking market share with profit are incomplete, and all three mechanisms can provide higher (or lower) explanatory power under different conditions. While, on average, market power provides the highest level, and firm efficiency the lowest level, of explanatory power, we also identify conditions under which the reverse is true (e.g., for young firms). Thus, none of the three theories from which the hypothesized mechanisms arise is ""correct"" or ""incorrect,"" but market power and quality signaling generally explain more of the variance in the market share–profit relationship across firms and industries.Second, our results offer new insights into efficiency-enhancing experience-based ""learning effects"" identified in strategic management theorizing ([ 2]). Management scholars have used this logic to explain why market share (a proxy for the number of times a firm may have produced a value offering) may be positively related to firm profit (e.g., [29]). We find that while firm efficiency is valuable (predicts profit), on average it is explained mainly by a firm's size rather than its market share. This suggests that for most firms, scale economies are more important in driving profit than economies of learning. However, for young firms, we find that market share delivers significant efficiency benefits above and beyond those associated with size, and we also find significant efficiency benefits from market share among service businesses. This suggests that ""learning by doing"" effects occur where organizational routines are less set and when firms can use experience gained to update and change their processes with lower investments.Third, we find support for information economics theorizing on the value of signals of unobservable firm quality. While prior research has explored market share's role in consumer evaluations of quality ([34]), we provide the first empirical evidence that market share generally signals firm quality and thereby increases firm profit. The negative effects on perceived quality we observe when using unit (vs. revenue) market share also suggest that price combines with market share in signaling quality to customers. In addition, we find that market share's positive quality-signal effect depends on previously unidentified industry and firm conditions (stronger for younger firms, in B2C markets, and for those with switching costs).For researchers, our study also has broader implications. Not least, it clearly shows the effect that sampling can have on the findings and inferences drawn in firm-level empirical research. We find wide variance in both the main market share–profit relationship and in the specific mechanisms accounting for the relationship across industries. Thus, samples made up of a single industry, or an industry dominated by certain types of firms, would lead to very different results and widely varying inferences being drawn as to which theory may be supported in empirical tests. This is unlikely to be unique to the market share phenomenon we examine. In addition, our study also reveals the desirability of directly observing (or at least finding direct indicators of) mechanisms believed to underlie relationships of interest. In particular, our results highlight the need for researchers to be careful about using indirect contingencies to infer such unobserved mechanisms when there may be more than one mechanism involved. Implications for PracticeThis study also provides new insights for managers regarding how market share should be measured. Although unit (volume) market share is widely used in practice to set marketing goals and monitor performance (e.g., auto and motorcycle manufacturers, many consumer packaged goods companies), our results reveal that it is not predictive of firm profit, whereas revenue (value) market share is. We also find that in terms of predicting profit, relative (to others) measures of revenue market share can be superior to absolute measures. Post hoc analyses suggest that such relative measures can enhance the market power value of the observed market share, and that benchmarking a firm's market share relative to the top three market share firms versus the market share leader offers a stronger predictor of future profit. This is aligned with the intuition that benchmarking against others provides an indicator of both the firm's market share and the concentration present in the marketplace, which we show interact significantly in predicting firm performance.To provide finer-grained managerial insights, we also examined ( 1) which measures of market share were the strongest predictors of future profit for different types of firms to help managers select the most appropriate market share metrics for goal setting and performance monitoring and ( 2) the average profit value of a 1% increase in the average firms' market share for different types of businesses to give managers a calibration of the dollar-value benefits that may be expected when evaluating costs associated with share building strategies. Given our sample size, we are somewhat limited in how fine-grained we can be in these analyses without running into power issues. We therefore split our sample in a managerially meaningful way by identifying firms on the basis of whether they serve primarily consumer or business customers and whether their value offerings are mainly product- versus service-based. As shown in Table 8, the results vary across the four cells, with B2C product firm and B2B service firm profit being most strongly predicted by absolute revenue market share, whereas for B2C service and B2B product firms, it is revenue share relative to the top three market share players. The one-year profit increases associated with a 1% improvement in the average firm's market share vary across the four cells from a low of just over $1 million to almost $6 million. These findings have clear and important implications for managers setting market share goals and monitoring market share performance in their firms and offer a useful dollar benefit scale calibration for managers with respect to the potential payoffs they may expect from investments in market share–building strategies.GraphTable 8. Managerial Matrix: Metrics. 26 Notes: Unit share is not predictive of firm profit in any one of the four cells. Reported elasticities estimated via a model specification equivalent to M3 in Table 2, with the noted strongest market share predictor measure as a regressor and using the observations specific to each of the Product/Services and B2C/B2B cells. Profit increase $ values are for a 1% increase in the mean firm's market share in each cell (e.g., 7.310% to 7.383%) not an increase of 1 point of total market share (e.g., from 7.310% to 8.310%). Because we estimate log-log models, the estimated coefficients in each condition can be interpreted as market share–profit elasticities (%) which can be converted to a dollar profit value by multiplying them by the mean profit in our sample (i.e., $840 million).In terms of where managers would be advised to pursue market share to a greater or lesser degree, our results provide several new insights (Table 9). For younger firms and for nonbanking services firms, it may make sense to set market share goals and monitor performance. It may also be more beneficial for firms operating in marketplaces with high levels of quality uncertainty and those with higher switching costs. However, it may make less sense for banks and firms in industries in which pricing power is low and/or quality is relatively certain. Older firms may also find market share to be of less value as a marketing goal and performance metric. Firms pursuing a niche strategy would be well advised to either ignore market share or ensure that they assess it only within their selected niche market definition. Finally, we show that, while relatively rare, ""buying share"" is not a profitable move.GraphTable 9. Managerial Matrix: Contingency Effects on Share-Profit Mechanisms. 27 Notes: n.s. = not significant. This table summarizes analyses reported in Table 4 and Web Appendix 12, with mechanism importance indicated relative to the average displayed by all firms in our sample. Implications for PolicyFor policy makers, this study provides new insights with respect to when market share may lead to market power and potential abuse that requires regulation. Importantly, our results show that firm profits from market share result from quality signaling and learning-based efficiencies as well as market power. Thus, policy makers need to be careful not to directly equate market share and market power; we show that while they are often related, they are far from synonymous. Rather, our results suggest that regulatory authorities can be less concerned by a firm's market share in marketplaces where customer quality uncertainty is significant and where efficiency-enhancing learning benefits from market share may exist (e.g., young firms, service firms). In such conditions, market share could enhance rather than harm consumer welfare by reducing consumer–firm information asymmetry and potentially lowering costs. Limitations and Future ResearchThis study has some limitations that should be taken into account when considering the findings. First, because we require public data to explore our research questions, our sample is naturally skewed toward larger firms. While we include small, nonpublic firms in the definition of the total market sales used in constructing the robustness check NAICS measure of market share, we are unable to include such firms' individual market shares in the hypothesis testing because these firms' sales data are private. Although we have a wide range of market shares in our sample (with a low of less than 1%, a high of 77%, and a mean of less than 7%), and no evidence of range restriction in our key variables, researchers with access to private firm data could test the generalizability of this study's findings to firms with much smaller market shares.Second, our data are focused on firms with U.S. listings. However, including studies covering broader geographies and longer time period data, E-H (2018) suggest that the market share–profit relationship is weaker in recent times in Western Europe than the United States, so future research in other regions is required to examine how the mechanisms we identify may differ across geographies. Third, our study examines market share at a firm level. However, market shares may also be computed at other levels (e.g., brand or geographic market level). A post hoc analysis of monobrand firms in our sample suggests that the same market share–profit main effect and mechanism relationships hold (Web Appendix 15); however, research is required to confirm this.Our study also reveals several new avenues for theoretically interesting and managerially relevant research. First, we find that the vast majority of market share's effect on profit is mediated through its effects on firm market power, perceived quality, and efficiency. This suggests that new theorizing regarding why market share is valuable may be of limited value. However, in light of our findings, new research on the details of how each of the three mechanisms works is clearly required. For example, what is the relative effect of market power on upstream versus downstream parties, and how much is contributed by cost reductions versus pricing versus coordination benefits? Similarly, what types and levels of quality uncertainty create conditions that lead to market share's value in signaling quality? How much of market share's signal value is to upstream versus downstream parties?Second, this study reveals market power, quality signaling, and operating efficiency as the mechanisms linking market share with firm profit. Because market share is a market-based outcome of firms' marketing efforts, this raises the interesting possibility that these three mechanisms may also mediate the relationship between other marketing-related phenomena and firm performance. For example, are market-based assets such as brand equity and customer relationships also linked to firm profit via the same three mechanisms? Are there also other mechanisms that may be available to such market-based assets but not to market share?Third, given that market share is more or less valuable under different market and firm conditions—and that buying share is both rare and ineffective—does it also matter how firms create and leverage market share? For example, are market shares more or less valuable to firms pursuing low-cost business strategies versus those pursuing differentiated advantages? Are the three mechanisms linking market share and profit the same for these different strategies, or are some mechanisms more important to one strategy than another? Addressing these questions would provide important new insights for both managers and researchers. Appendix: Measure DetailsGraph Appendix: Measure Details 28 Notes: SEC = Securities & Exchange Commission; SGA = selling and general administrative. " 21,Faculty Research Incentives and Business School Health: A New Perspective from and for Marketing," Grounded in sociological agency theory, the authors study the role of the faculty research incentive system in the academic research conducted at business schools and business school health. The authors surveyed 234 marketing professors and completed 22 interviews with 14 (associate) deans and 8 external institution stakeholders. They find that research quantity contributes to the research health of the school, but not to other aspects of business school health. The r-quality of research (i.e., rigor) contributes more strongly to the research health of the school than research quantity. The q-quality (i.e., practical importance) of research does not contribute to the research health of the school but does contribute positively to teaching health and several other dimensions of business school health. The authors conclude that faculty research incentives are misaligned: ( 1) when monitoring research faculty, the number of publications receives too much weight, while creativity, literacy, relevance, and awards receive too little weight; and ( 2) faculty feel that they are insufficiently compensated for their research, while (associate) deans feel they are compensated too much for their research. These incentive misalignments are largest in schools that perform the worst on research (r- and q-) quality. The authors explore how business schools and faculty can remedy these misalignments.","Business schools consider academic research by their faculty as one of the main pillars in their business model and allocate a large part of their resources to it (e.g., faculty time, labs, research budgets). At the same time, prior research across fields, including marketing, has heavily debated whether the academic research that business school professors conduct adds value to the business schools that employ them (see Table 1[ 5]).GraphTable 1. Selected Papers on the Role of Academic Research in Business Schools. 1 a We constrained the selection of articles in Table 1 to those published in journals on the UTD list.2 b We collected the number of Google Scholar citations for the listed papers on February 1, 2021.3 c While many papers cover multiple dimensions, we attempted to define the focus of the respective papers rather narrowly. The four dimensions we categorize papers on are ( 1) ""consequences of research for business schools,"" which includes papers that explicitly take a business school perspective (as contrasted to a field perspective); ( 2) ""practical importance of research,"" which includes papers that address threats to q-quality (e.g., the gap between academia and practice) from the perspective of academics; ( 3) ""adoption of research by practice,"" which includes papers that address the limited application of academic research, from the perspective of practitioners; and ( 4) ""rigor of research,"" which includes papers that address threats to r-quality (e.g., low replicability of studies, low rigor and scientific integrity of research).4 d We included these articles published in journals outside the UTD list as an exception to the rule, because of their strong impact.On the positive side, academic research may enhance a professor's relevant knowledge base, which can be transferred to students and motivate them to study the subject ([34]). Academic research may also signal teaching quality to high-quality prospective students ([12]). Business school faculty or deans may also advocate certain schools on the basis of their academic research performance, thus affecting school choices and driving high-quality students and faculty to research-intensive schools ([34]). On the negative side, scholars have lamented the lack of practical importance of business school research (e.g., [26]; [31]; [43]; [52]). In addition, science fraud cases in business schools have called into question the integrity and rigor of academic research in management ([13]).Prior literature has hinted that the faculty research incentive system of business schools, composed of monitoring and compensation instruments, may be responsible for the main concerns on rigor (formally, r-quality) and practical importance (formally, q-quality) that are voiced about business school research ([29]; [31]; [41]; [60]). The purpose of this article is to examine the effects of the faculty research incentive system on the execution of the research task by faculty and, thereby, on a holistic set of business school outcomes, which, following prior work in the educational literature (e.g., [25]), we conceptualize as ""business school health."" Business school health is the extent to which a business school performs well ( 1) at the technical level (i.e., research and teaching), ( 2) at the institutional level (i.e., external support and institutional integrity), and ( 3) at the managerial level (i.e., leadership support, administrative support, and resource support). We define all key terms in Table 2.GraphTable 2. Key Construct Definitions and Representative Papers. 5 Notes: For a complete set of construct definitions and corresponding operationalizations, see Table W1 in the Web Appendix, section W2.This research offers two main contributions. First, many articles take a scholarly field perspective rather than a business school perspective. Exceptions ([11]; [34]; [39]; [56]) focus on specific business school outcomes (e.g., master of business administration [MBA] ranking) or specific research metrics (e.g., number of publications) and often contradict each other, with some being very negative and others being more positive. This article also takes a business school perspective, but it offers more elaboration on faculty research incentives, faculty research task, and business school outcomes (i.e., business school health) than prior research. Second, prior work suggesting that the faculty research incentive system is one of the main culprits for today's state of affairs (see, e.g., [31]; [41]; [60]) did not theoretically conceptualize this faculty research incentive system or offer empirical evidence of its misalignment. This article does both.We theoretically ground our hypotheses in sociological agency theory ([49]). We provide empirical evidence from ( 1) a survey of 234 marketing professors in business schools across 20 countries (response rate of 62.6%), ( 2) qualitative interviews with 14 (associate) deans of 13 business schools in the United States and Europe, and ( 3) qualitative interviews with 8 external stakeholders representing external institutions of marketing scholarship (e.g., the American Marketing Association) and marketing practice at large multinational firms.Our main conclusions are as follows. Research task incentives are badly designed, on average. Among monitoring instruments, we find that business schools, on average, overweight number of publications in faculty evaluations while creativity, literacy, relevance to nonacademics, and awards (in order of importance) receive too little weight. Among compensation instruments, we find, on average, that faculty feel they are insufficiently compensated, whereas (associate) deans feel that faculty are compensated too much for their research. We find that badly designed incentive systems are more prevalent in schools that perform below the median on research quality—that is, r-quality (i.e., rigor) and q-quality (i.e., practical importance). We do not find such a relationship between badly designed research incentives and research quantity.Regarding the research task of the faculty, we find that research quantity contributes to business school research health but not to other aspects of business school health. The r-quality of research contributes more strongly to business school research health than research quantity and q-quality of research. The q-quality of research does not contribute to business school research health but does contribute positively to business school teaching health as well as several other dimensions of business school health, such as external support (by alumni and donors) and institutional integrity.Our findings have important implications for business schools and research faculty. First, business schools need to develop better research metrics to monitor the academic research of their faculty. Second, business schools need to improve alignment with their faculty on compensation. Third, business schools need to improve the quality (especially q-quality) of their faculty's research. We provide specific suggestions how business schools can follow up on each of these three main implications. Faculty Research in Business Schools: A Sociological Agency FrameworkWe develop a sociological agency framework on business school research (see Figure 1), in which we distinguish four elements: ( 1) constituents (e.g., principal, agents, institutions), ( 2) incentive instruments[ 6] the principal uses to motivate the agent (e.g., publication metrics), ( 3) the task of the agent (e.g., research), and ( 4) desirable outcomes for the principal (e.g., business school health).Graph: Figure 1. A sociological agency theory perspective on academic research in business schools. Constituents: Principal, Agents, and InstitutionsThe business school is a ""collective principal,"" comprising a chain of delegation in a system of peers, akin to complex administrative structures often found in international organizations (e.g., [35]). Business schools typically operate within a university, which oversees the school's incentive system (exceptions exist, e.g., INSEAD) and are divided into disciplinary units or departments, each of which influences the school's incentive system (see top of Figure 1). The agent in our framework is a research or tenure-track faculty member. The business school incentivizes the research of agents by monitoring and compensating the faculty member's research task.External institutions are organizations outside the governance of the business school that play an essential role in social monitoring because principal–agent relationships are ""enacted in a broader social context and buffeted by outside forces"" ([49], p. 269).[ 7] Building on [ 2], we discern two external institutions of special relevance[ 8]: ( 1) endorsement institutions and ( 2) cohesion institutions (see the bottom of Figure 1; for a primer and nonexhaustive list of these institutions in the marketing field, see section W1 in the Web Appendix).Endorsement institutions verify information about agents, conduct analyses to compare or rank agents, and endorse agents. Examples of such institutions in marketing that endorse faculty are premier journals that publish their research (e.g., the Journal of Marketing) or associations (e.g., the American Marketing Association [AMA]) that have a variety of awards for research. Cohesion institutions ensure collective action by enabling the provision of collective goods. Collaborative research platforms, such as the Marketing Science Institute (MSI) or Institute for the Study of Business Markets (ISBM), are good examples of such cohesion institutions (note that institutions can provide endorsement as well as cohesion, as is the case for the AMA). Incentives: Monitoring and Compensation InstrumentsPrincipals use monitoring instruments to measure an agent's effort or outcomes ([27]), of which the following are relevant for business school research (e.g., [30]): ( 1) number of publications, ( 2) number of citations, ( 3) peer recognition, ( 4) awards, ( 5) relevance to nonacademics, ( 6) literacy,[ 9] and ( 7) creativity. Compensation instruments are the rewards, pecuniary and nonpecuniary, that principals use to align the actions of agents with their own objectives, of which the following are relevant for business school research (e.g., [21]): ( 1) salary, ( 2) performance-based salary increases, ( 3) publication bonuses paid as salary supplements,[10] ( 4) research budgets, ( 5) publication bonuses paid as supplementary research budget,[11] ( 6) academic freedom, and ( 7) reduced teaching loads. Task: Faculty ResearchThe faculty research task is to produce research of sufficient quantity (""doing enough research"") and quality (""doing research that is good enough""). Research quantity relates to the total volume of research produced by a scholar (e.g., [30]). For research quality, we distinguish ""r-quality"" from ""q-quality"" ([17]). Academic research is of high r-quality (i.e., rigorous) if it adheres to ""objective, scientific standards"" ([11], p. 99), which means that ""the various elements of a theory are consistent, that potential propositions or hypotheses are logically derived, that data collection is unbiased, measures are representative and reliable, and so on"" ([61], p. 755). Academic research is of high q-quality (i.e., practically important) if it provides insights that ""practitioners find useful for understanding their own organizations and situations better than before"" ([61], p. 755). Outcome: Business School HealthBuilding on the classic work of [38] and [25], we define a healthy business school as a business school that performs well at three levels: ( 1) the technical level, ( 2) the institutional level, and ( 3) the managerial level. At the technical level, a healthy business school has high research health (i.e., research faculty are seen as leading in their respective fields, publish regularly in leading journals, and assume academic leadership positions) and high teaching health (i.e., the school offers an excellent learning environment with high standards for teaching). At the institutional level, a healthy business school has high external support (i.e., very good relationships with alumni and donors, who commit substantial resources to the school) and high institutional integrity (i.e., faculty and students uphold the highest standards of integrity). At the managerial level, a healthy business school has strong leadership support (i.e., a high-quality leadership team and clear faculty performance standards), strong administrative support (i.e., professional administrative staff that is supportive to faculty, students, and visitors), and strong resource support (i.e., adequate facilities and resources to help faculty effectively perform their work). Hypothesis DevelopmentNext, we develop our hypotheses, starting with the effects of incentive instruments on the research task of the faculty,[12] after which we turn to the effects of the research task on business school health (for a graphical overview, see Figure 2).Graph: Figure 2. The effect of the faculty research incentive system on the research task of the faculty and business school health. Incentive Instruments and the Research Task of the FacultyAccording to agency theory (e.g., [23]), incentive instruments increase an agent's motivation by raising the marginal cost of bad performance (through monitoring) and/or the marginal reward of good performance (through compensation). Higher motivation, in turn, leads the agent to work harder and to perform better on their task. However, there are multiple reasons to expect the effect of incentive instruments on the research task of professors in business schools to be more nuanced.Incentive instruments may be improperly weighted and deviate from what both agents and principals see as the optimal incentive system, because optimal incentives are typically costly to design and implement ([27]). For instance, often, quality is more expensive to monitor than quantity ([23]). In the context of business schools, an increasing number of automated scientometric tools make the monitoring of research quantity inexpensive, while the monitoring of research quality remains expensive for multiple reasons: ( 1) it is more difficult to objectify quality than to objectify quantity, ( 2) it is more difficult to compare research quality across domains than to compare research quantity across domains, and ( 3) senior business school administrators may have been detached from high-quality research activities themselves for a long time. Consequently, business schools may design incentive systems that overweight research quantity, possibly at the expense of research quality.Incentive systems that overweight quantity may lead faculty to become extrinsically motivated to publish as many papers as possible, possibly leading them to ignore quality ([23]) or to engage in undesirable practices to game the metrics rather than optimize the task itself. An example is ""salami publishing"" (i.e., trying to squeeze as many papers as possible out of a research project). Therefore, we expect improperly weighted incentive instruments to increase the quantity of faculty's research.However, such an increase in quantity may come at the expense of a decrease in (r- and q-) quality of the faculty's research. Badly designed incentive systems reduce the intrinsic motivation of the agent because agents in badly designed incentive systems may feel underappreciated, which impairs self-esteem, or externally pressured, which impairs self-determination ([19]). Impaired self-esteem reduces agents' persistence in difficult tasks ([33]), which is critical to improve or sustain r-quality ([ 3]; [17]). Impaired self-determination reduces creativity ([ 5]), which is an important precursor to q-quality ([51]). For instance, [14], p. 5) argues that ""home run"" papers ""pose new questions that we had never thought to ask"" or ""allow us to see existing problems and solutions from a new perspective."" Therefore, we hypothesize: H1: In business schools with improperly weighted incentive instruments, research faculty (a) produce a higher quantity of research, (b) produce research of lower r-quality, and (c) produce research of lower q-quality compared with business schools with properly weighted incentive instruments. The Research Task of the Faculty and Business School HealthNext, we postulate the effects of the research task of the faculty on research health and teaching health as well as on external support and institutional integrity. We do not develop ex ante expectations for the managerial level of business school health.[13] The research task of the faculty and research health of the business schoolScholars who publish a high research quantity (controlling for quality) have higher visibility than scholars who publish a low research quantity ([54]). Scholars who frequently ""survive"" peer review also demonstrate to others they know ""what is needed, correct, and valued"" by the research system ([29], p. 156) and typically attract more collaborations, increasing their belongingness to the academic community. Higher visibility and belongingness increase the extent to which a scholar attains academic leadership. Therefore, we hypothesize: H2: The research health of a business school increases with the production of a higher quantity of research by its research faculty.For research quality, the effect on research health may be more nuanced; we expect increases in r-quality of faculty's research to contribute more strongly to research health of a business school than increases in q-quality of faculty's research. [ 1] show that scholars acclaim stronger reputational rewards to basic than to applied science because basic research requires a higher level of scientific ability than applied research. Basic research is typically higher in rigor than applied research, which, in turn, is typically higher in practical importance ([58]). [ 3] calls this the ""hardness bias,"" which he also attributes to the greater agreement among scholars on r-quality than on q-quality. In turn, the greater reputational rewards faculty may derive from increments in r-quality, as compared with increments in q-quality, fuel opportunities to take up leadership roles in journals and in the academic research community ([17]). Therefore: H3: The research health of a business school increases more as research faculty produce research of higher r-quality than as research faculty produce research of higher q-quality. The research task of the faculty and teaching health of the business schoolResearch quantity may have two opposite effects on teaching health. On the one hand, a high volume of research may give faculty members a broader knowledge base in their teaching subjects, increasing their ability to set high teaching standards and to motivate students' interest in the subject ([34]). On the other hand, research and teaching activities compete for faculty time. Assuming a time constraint, the more research faculty allocates time to writing papers, the less they allocate time to preparing classes, creating teaching materials, and meeting with students. [12] analytically show that increasing research output may deteriorate teaching quality. Therefore, we formulate two alternative hypotheses: H4a: The teaching health of a business school increases as research faculty produce a higher quantity of research. H4b: The teaching health of a business school decreases as research faculty produce a higher quantity of research.Faculty members who produce research high in q-quality typically immerse themselves in real-world managerial practice through consulting, case writing, or executive education ([61]). Such immersion, in turn, increases a faculty member's usage of concrete concepts, which are easier to understand than abstract concepts ([57]). In contrast, high r-quality faculty tends to abstract from contextual details to focus on the key underlying properties of a situation or problem ([29]). Moreover, the strong theoretical and methodological grounding of high r-quality faculty may lead them to underestimate that abstract concepts may not be obvious to less informed audiences. Therefore, we expect faculty who produce research high in q-quality (high in r-quality) to use more concrete (more abstract) concepts when teaching students. Teaching in concrete rather than abstract language is more effective because it enhances student comprehension and memory retention ([47]), which, in turn, may ensure high teaching standards. Therefore, we hypothesize: H5: The teaching health of a business school increases more as research faculty produce research of higher q-quality than as research faculty produce research of higher r-quality. The research task of the faculty and external support to the business schoolWe expect increases in research quantity to contribute less to external sponsors' (i.e., alumni and donors) willingness to donate their time or money to the school than increases in research (r- and q-) quality. Using self-reported data from alumni, [32] show that donors' self-esteem increases more when they donate to a high-prestige than to a low-prestige school. The production of high-quality research is a more important driver of the prestige of an academic institution than the production of a high quantity of research ([15]), for two main reasons.First, a rare favorable outcome (e.g., publishing a ""home run"" paper) conveys more information about an individual's ability than being able to achieve several less favorable outcomes ([50]). Thus, research quality is more significant than research quantity in eliciting recognition through awards, appointments to prestigious academic departments, and overall prestige among national and international peers ([15]).Second, the awards and accolades bestowed to high-quality scholars serve as signals of appreciation and recognition by external experts. [32] argue that academic institutions can symbolically manage such quality signals as ""identity anchors"" that increase the salience of the institution among alumni and donors and, ultimately, their willingness to support the institution. Accordingly, we hypothesize: H6: The external support for a business school increases more as research faculty produce research of higher quality (in both r- and q-quality) than as research faculty produce a higher quantity of research. The research task of the faculty and institutional integrity of the business schoolFaculty who conduct research high in r-quality are more likely to adopt and disseminate the latest scientific guidelines that heavily endorse research integrity ([36]) and, in turn, may develop a stronger overall ""moral muscle"" that transcends domains ([ 9]) than faculty who conduct research low in r-quality. Faculty with a stronger moral muscle may more effectively disseminate ethical values to students. Therefore, we hypothesize: H7: The institutional integrity of a business school increases as research faculty produce research of higher r-quality.We do not have theoretical expectations regarding the effects of research quantity and q-quality on a business school's institutional integrity. We explore such effects empirically. Other effectsWe control for several other effects in our empirical tests. First, we empirically explore the effects of the research task of the faculty on the managerial level of business school health, for which we did not posit ex ante expectations. Second, we allow for correlated error terms when we estimate the effects of the research task of the faculty on the different dimensions of business school health. In this manner, we accommodate for the existence of feedback loops that we conceive in two main ways (see right-to-left arrows at the top of Figure 2): ( 1) business school health may influence the faculty in the execution of their research task and ( 2) the faculty's execution of the research task may lead to adjustments in monitoring and compensation. Empirical StudiesIn this section, we provide empirical evidence from surveying marketing faculty members and interviewing (associate) deans of business schools and external stakeholders. Study 1: A Large-Scale Survey of Marketing Research Faculty at Business Schools Data collectionWe invited 374 marketing academics across 168 business schools to respond to our survey; 234 responded (62.6%). Of these, 182 (77.8%) respondents work at research-intensive schools (i.e., schools where tenure criteria are mainly research focused) and 149 of the respondents (63.7%) work at business schools that are ranked in the Top 100 Financial Times (FT) Global MBA ranking. For further details on survey sampling, questionnaire structure, analysis, and results, see section W2 in the Web Appendix and visit www.frisbuss.com.[14] MeasurementRegarding the faculty research incentive system, we asked respondents if, at their school, each of the seven monitoring instruments we study receives far too little weight (−2), too little weight (−1), just the right weight (0), too much weight (+1), or far too much weight (+2). Regarding compensation, we asked respondents whether they felt research faculty at their school receive far too little (−2), too little (−1), just the right level (0), too much (+1), or far too much (+2) of each of the seven compensation instruments we study.Regarding the research task of the faculty, we asked respondents whether the performance of research faculty at their business school in each of the three dimensions of the research task (i.e., research quantity, r-quality, and q-quality of research) was ""very low,"" ""low,"" ""moderate,"" ""high,"" or ""very high.""To measure business school health, we created a 21-item scale (see Table 3) by adapting earlier measures of [25] to the business school context. We conducted a principal component analysis with varimax rotation on this scale. The scree plot suggested a seven-component structure with all items loading on their expected theoretical dimensions.[15] The seven components accounted for 82.6% of the total variance, with the largest component accounting for 12.7% of the total variance. All loadings were greater than the recommended threshold of.60, with the lowest being.73 (see Table 3). Next, we conducted a seven-factor confirmatory factor analysis (CFA; χ2 = 251.08, p <.01, d.f. = 168). The fit indices for this model meet the recommended standards (comparative fit index [CFI] =.98, nonnormed fit index [NNFI] =.97, root mean square error of approximation [RMSEA] =.05, square root mean residual [SRMR] =.04).[16]GraphTable 3. Business School Health Scale Items and Factor Loadings. 6 a Loadings from a principal component analysis with varimax rotation on the full set of 21 items without predetermined factors. Each item had its highest loading in its theorized factor, which we report here.7 b Standardized loadings obtained from a confirmatory factor analysis with items preloaded on the seven business school health dimensions.8 Notes: CR = composite reliability ([ 7]); AVE = average variance extracted ([18]).Overall, our business school health scale exhibits good psychometric properties. All seven dimensions show composite reliabilities above the recommended threshold of.70 ([ 7]), the smallest being.80 (see Table 3). All factor loadings were positive, highly significant (minimum z-value was 18.95; all p-values below.01), and at least ten times as large as the standard errors establishing convergent validity ([20]). For all pairs of business school health dimensions, the square root of the average variance extracted for both dimensions was greater than their correlation, which demonstrates acceptable discriminant validity ([18]). We averaged respondents' answers across each set of three items for each business school health dimension to produce seven summated scales. Common method variance (CMV) biasWe addressed CMV ex ante by ( 1) promising confidentiality to respondents ([40]), ( 2) using well-defined response labels that varied across questions ([42]), and ( 3) asking respondents to evaluate their business school's performance rather than their own performance, triggering high involvement and informant reliability ([24]). Ex post, we found that ( 1) the largest factor in our principal component analysis accounted for only 12.7% of the variance explained, and ( 2) a single-factor CFA model fits the data worse than our hypothesized model (CFI =.49, NNFI =.43, RMSEA =.20, SRMR =.12). Both findings are inconsistent with severe CMV. Results: incentive instruments and the research task of the facultyFigure 3 shows the average value (μ) for each incentive instrument. The asterisks depict whether this value is significantly different from 0; 0 indicates that the weight given to that instrument is ""just right."" On average, Figure 3 shows that business schools' research incentive systems are badly designed.Graph: Figure 3. Misalignment of incentive instruments.* p <.10.** p <.05.*** p <.01.aThe question asked for each monitoring instrument was ""At your school, do you feel that the following metrics on research faculty receive too much or too little weight?"" (−2 = ""Far too little weight,"" −1 = ""Too little weight,"" 0 = ""The weight is just right,"" +1 = ""Too much weight,"" and +2 = ""Far too much weight"").bThe question asked for each compensation instrument was ""At your school, do you feel that research faculty receive too little or too much of each of the following as rewards for their research?"" (−2 = ""Far too little,"" −1 = ""Too little,"" 0 = ""Just right,"" +1 = ""Too much,"" and +2 = ""Far too much"").Notes: The asterisks represent the p-values for t-tests comparing the mean score for the perceived appropriateness of the weight given to each instrument to 0 (which means the weight is ""just right""). All p-values are two-sided. In the case of compensation questions, respondents could answer ""not applicable""; thus, we indicate the sample used to compute mean responses next to each compensation instrument's label in the right panel.Of the monitoring instruments (Figure 3, Panel A), we find that the ""number of publications"" receives too much weight (μ =.39; t = 6.88, p <.01). All other monitoring instruments receive too little weight, especially so for (in order) ( 1) creativity (μ = −.65; t = −12.95, p <.01), ( 2) literacy (μ = −.49; t = −10.05, p <.01), and ( 3) relevance to nonacademics (μ = −.44; t = −8.58, p <.01).Of the compensation instruments (Figure 3, Panel B), we find that respondents consider research faculty at their school to be insufficiently compensated, except for the academic freedom they get, especially so for (in order) ( 1) bonuses paid as research budget (μ = −.84; t = −10.34, p <.01), ( 2) bonuses paid as salary (μ = −.77; t = −9.47, p <.01), and ( 3) reduced teaching loads (μ = −.67; t = −10.36, p <.01).To test H1, we first generated a 2 × 2 matrix according to a median split of respondents as below median or above median in terms of the performance on research quantity and r-quality of their business school (Figure 4, Panel A). Then, for each respondent, we computed the mean absolute deviation (MAD) from 0, aggregated across all seven monitoring and compensation instruments.[17] We then averaged these individual scores to obtain MADM and MADC for each of the cells in the 2 × 2 matrix.Graph: Figure 4. Misalignment of incentive instruments: variation according to research quantity and r-quality and q-quality.Notes: To measure whether faculty research incentive instruments are improperly weighted (i.e., misaligned) we computed mean absolute deviations (MAD). Specifically, we first computed individual MAD scores, which are the averages of the absolute deviations between a respondent's scores in all items of a given scale (say, all seven monitoring instruments) and the central point of the scale (which indicates that the weight given to a given instrument is ""just right""). The values reported in this figure are the averages, across respondents in a given cell, of these individual MAD scores for monitoring instruments (MADM) and for compensation instruments (MADC). To avoid a skewed split, we randomly classified respondents in the ""median category"" (e.g., those with a score of 4 for research quantity) as either ""below median"" or ""above median"" using a proportion that ensures that approximately half of the respondents are classified as ""below median"" and the other half as ""above median"" in each dimension.We ran two one-way analyses of variance of the MADM and MADC by respondents across the four cells in Figure 4, Panel A. Fisher–Hayter post hoc tests[18] show that there are no significant differences in the extent to which incentive instruments are properly weighted (i.e., MADM and MADC) in schools with above-median versus below-median research quantity (see Web Appendix, section W2). Thus, we are not able to confirm H1a.Consistent with H1b, Fisher–Hayter post hoc analyses show that in schools with above-median r-quality (i.e., upper cells in Figure 4, Panel A), monitoring instruments are more properly weighted (i.e., lower MADM) than in schools with below-median r-quality (i.e., lower cells), an effect that is significant both at low levels of research quantity (p <.05) and at high levels of research quantity (p <.01). Compensation instruments are more properly weighted (i.e., lower MADC) in schools with above-median r-quality (i.e., upper cells) than in schools with below-median r-quality (i.e., lower cells), an effect that is significant at the 10% level at low levels of research quantity (p <.10) and approaches significance at high levels of research quantity (p =.14).We used the same approach to generate a 2 × 2 matrix according to a median split on research quantity and q-quality (Figure 4, Panel B). We then ran two one-way analyses of variance of the MADM and MADC by respondents across the four cells in Figure 4, Panel B. Consistent with H1c, Fisher–Hayter post hoc analyses show that in schools with above-median q-quality (i.e., upper cells in Figure 4, Panel B), monitoring instruments are more properly weighted (i.e., lower MADM) than in schools with below-median q-quality (i.e., lower cells), an effect that is significant at low levels of research quantity (p <.05) but not at high levels of research quantity (p =.18). We do not find such a contrast for compensation instruments (MADC). Results: the research task of the faculty and business school healthTo test H2–H7, we estimated a multivariate regression system of the seven dimensions of business school health on the three dimensions of the research task (research quantity, r-quality, and q-quality), with correlated error terms across the seven equations (see the Web Appendix, section W2). The Lagrange multiplier test proposed by Breusch and Pagan confirms that the covariance matrix between error terms is not diagonal (χ2(21) = 573.8, p <.01). The fit of the model is satisfactory. The R2-statistic is highest for research health (.46), which befits the primary focus of our investigation.We depict our results in Table 4. The first four rows show the parameter estimates of research task on business school health, whereas the subsequent seven rows show the residual correlations among the different business school health dimensions. Confirming H2, we find that higher research quantity is associated with higher research health (β =.28; p <.01). We also find that the higher the r-quality of faculty research, the higher the research health of a business school (β =.52, p <.01). In contrast, q-quality has no significant effect on research health (β =.05; p =.32). A Wald test rejected the null hypothesis that the parameters for r- and q-quality are equal (F = 10.11, p <.01), thereby confirming H3.GraphTable 4. Impact of Faculty Research on Business School Health. 9 *p <.10.10 **p <.05.11 *** p <.01.12 Notes: All p-values are two-sided. The first four rows depict the parameter estimates from our multivariate regression. The subsequent seven rows depict the correlations obtained from the residual correlation matrix. We rely on a multivariate regression because it allows us to jointly estimate the seven models as one regression system while accounting for error correlations. Multivariate regression is a special case of Zellner's seemingly unrelated regression with identical regressors across equations, in which case the seemingly unrelated regression estimator simplifies to ordinary least squares in each equation. Yet, because it is a joint estimator, the multivariate regression also estimates between-equation error correlations, allowing us to efficiently test coefficients across equations.Confirming neither H4a nor H4b, we find that a higher research quantity does not have a significant effect on teaching health (β = −.03, p =.67). We also find that a higher q-quality of faculty research is associated with higher teaching health of a business school (β =.21, p <.01), whereas higher r-quality is not (β =.02, p =.69). A Wald test rejected the null hypothesis that the parameters for r- and q-quality are equal (F = 4.53, p <.05), thereby confirming H5.Confirming H6, we find that research quantity may negatively affect external support (β = −.17, p <.10), while higher levels of r-quality (β =.21, p <.01) and of q-quality (β =.27, p <.01) positively affect external support. A Wald test showed that the coefficients for r-quality and q-quality are not significantly different from one another (F =.28, p =.59).Confirming H7, we find a positive effect of r-quality (β =.17, p <.05) on institutional integrity. We find no significant effect of research quantity on institutional integrity (β =.06, p =.44) and a positive and significant effect of q-quality on institutional integrity (β =.25, p <.01).As to other effects, we observe that schools with high r-quality research have strong leadership support (β =.36, p <.01), strong administrative support (β =.26, p <.01), and strong resource support (β =.22, p <.01). Schools with high q-quality research have strong leadership support (β =.14, p <.05), administrative support (β =.18, p <.01), and resource support (β =.10, p =.12). We do not find any association between research quantity and leadership support (β =.12, p =.18), administrative support (β = −.04, p =.64), or resource support (β =.01, p =.91). Study 2: In-Depth Interviews with (Associate) Deans and External StakeholdersNext, we report on the interviews we conducted with (associate) deans and with representatives of external institutions. These interviews took 35 minutes on average and yielded a total of 164 pages of single-spaced transcripts. Interviews with (associate) deansWe conducted phone interviews with seven deans (four former and three current) and seven associate deans (two former and five current) at 13 business schools in the United States and Europe (for more information, see section W3 in the Web Appendix), who are good informants ([24]). We opted for a ""phenomenological"" approach that is in-depth but nondirective in nature ([55]). We audio-recorded the interviews (except for two who did not give permission), which were subsequently transcribed by a research assistant and double-checked by one of the authors for accuracy. Our interviews led to the following insights.First, virtually all (associate) deans we interviewed expressed that there is an overreliance on effortless metrics (especially counting number of publications, but also number of citations) often at the expense of more effortful metrics such as creativity, literacy, and relevance to nonacademics. Of the 14 (associate) deans we interviewed, 11 recognized this overreliance on effortless metrics, and 9 explicitly mentioned they saw this trend as problematic for business schools, as highlighted by the following quotes:I definitely have seen just what I feel is an overreliance on the cohort table and the numbers. And I feel that that was something that I have kind of raised but I do not feel that I necessarily had any impact in terms of trying to say this is just one piece of information. (Former vice-dean for faculty at a U.S. FT Top 25 school)When I started in 2000–2001, it was about the quality of the journals and what the outside reviewers said. So initially, there was very light weight put on citation counts, and then over time, it started to increase a bit and then we got a couple of people elected to the promotion and tenure committee who were like, ""We don't even have to look at quality, we can tell from the citation counts whether these things are any good or not."" (Former dean at a U.S. FT Top 30 school)[Awards] should weigh a lot even when compared with contemporary productivity metrics, but in all honesty, contemporary productivity metrics are some of the most overused metrics to gauge academics. (Current dean of research at a non-U.S. FT Top 75 school)My frustration is, when I'm drawing on a department chair for information, I get counts such as they had 27 publications, 4 in premier outlets, and this was the citation count. (Current dean at a large U.S. public school)I remember when Google Scholar first came out, there was a lot of skepticism about it...but that has definitely been adopted as the norm. And I think the ease of checking it and following it has caused a drift toward weighing it more heavily. (Former dean at a U.S. FT Top 15 school)Are we just giving up on our ability to be doing all the heavy work? I think we are relying too much on the ease of numbers. (Current dean at a U.S. FT Top 75 school)I personally view it [a growing reliance on counting] as a very negative trend because people start gaming the citation count. (Current dean at a U.S. FT top 100 school)Now that we have metrics and now that people are scored on those metrics, I think that the system does—it shouldn't, but it does—put a greater emphasis on those numbers and less on, for example, creativity. (Current vice-dean at a U.S. FT Top 10 school)Second, 9 out of the 14 (associate) deans we interviewed found business school professors overpaid for the research they do, in contrast with the views of research faculty in our survey. The following three quotes illustrate their views:People come with their hands out all the time. I do not get it. It is just wrong. And I think we get paid really well. We have been historically. And we get things that other university faculty just do not get, like guaranteed summers. I mean, talk to someone in public health, right? It has become an absurdity to me, and it's very unsustainable. (Current dean at an FT Top 75 school)The financial incentives that exist right now in the field are, to a certain extent, disturbing the market. I think the financing model of the top 100 business schools in the U.S. sooner or later will explode....It is a crisis waiting to happen. (Current dean of research at a non-U.S. FT Top 75 school)Nowadays, it is too hard to get faculty to do things, so you start compensating, paying for everything. (Current dean at a large U.S. public school)Nearly all the (associate) deans we interviewed also expressed a negative opinion on publication bonuses, again in contrast with research faculty in our survey. The following two quotes are representative of this generalized negative feeling:We do not have bonuses for publications, and I do not find those a good idea; they may trigger perverse behaviors. (Current vice-dean at a public non-U.S. business school)I think that, at least among our faculty, if a bonus were paid directly for a paper, it would make faculty feel like coin operated. And I think that would lead to a culture impact that would not serve us. (Former dean at a U.S. FT Top 15 school)Third, the interviews largely confirmed that research quantity and research quality (both in r-quality and q-quality) are important for a business school's research health. Nine of our interviewees expressed a more positive view on the extent to which their school's faculty was achieving this on r-quality than on q-quality:Basic science tries to understand how the world works, applied science tries to develop applications. I believe that management research is now 99% ""basic"" and only 1% ""applied."" (Current vice-dean at a public non-U.S. business school)We like to see people who hit a home run, like, ""this is a really good paper.""...There's a lot of acceptance of low productivity rates if the quality of the home runs is there. (Former deputy dean at a U.S. FT Top 30 school)I feel increasingly frustrated by the extent to which we talk to other academics and we do work that is not addressing the issues and questions that are really most pressing in the world of business or the world more broadly, and that we could be a lot more relevant and we could be speaking to practice a lot more. (Former vice-dean for faculty at a U.S. FT Top 25 school)At some level, most of the work that I see that goes on doesn't connect to management....Sometimes the research is so technical that it's not acceptable to a broader audience. (Former deputy dean at a U.S. FT top 30 school)When I look at what's in the journals, it strikes me that most of it is pretty irrelevant to what's going on in the world. So, I think that's a huge issue. (Former dean at a U.S. FT Top 30 school)Fourth, while basically all the (associate) deans we interviewed viewed teaching health as fundamental, four of our interviewees expressed concerns with the impact of the research task of the faculty on teaching health, as illustrated by the following two quotes:We have a management department...and I think at this point, there's maybe two people in there who could be teaching exec ed. And that is where your leadership people should be...and they just can't do it. At some level, we may kick ourselves out of business. (Current dean at a U.S. FT Top 75 school)It seems every marketer wants to be a social scientist and wants to stop selling cookies. I mean, there are a lot of marketing scholars that fundamentally do not study marketing topics anymore and just look at topics that are generic social science research topics. (Current dean of research at a non-U.S. FT Top 75 school) Interviews with external stakeholdersWe conducted phone interviews with eight external stakeholders including ( 1) current or past leaders at five external institutions of marketing scholarship (e.g., MSI); and ( 2) senior marketing practitioners at three large multinational firms (the former global chief marketing officer of a large multinational technology corporation, the current chief executive officer [CEO], and an executive vice president [EVP] at two of the world's largest market research firms), who are or have been involved with these external institutions.The interviews with external stakeholders yielded the following key insights. First, consistent with our theorizing, interviewees expressed that business schools track endorsement institutions' monitoring of their faculty. As a former chair-elect of the AMA Board of Directors pointed out:[The] AMA aims to promote the creation of cutting-edge marketing content both through the journals and through the awards inside the Foundation. Faculty go back and list those awards on their annual reviews and use that as part of their argument for where they should stand inside their institution.Second, again consistent with our theorizing, interviewees confirmed that cohesion institutions enable the provision of a common base of knowledge, sharing of such knowledge, data access, or connections to practice, all of which supports research faculty in their research agenda.[19] For instance,MSI's Young Scholars program helps juniors develop a strong cohort. They get more invited to talks, it gets them the opportunities to be recruited, and it starts research collaborations. (Former executive director of MSI)I think institutions such as MSI or the ISBM can facilitate research that has both academic rigor and has got practical merit. (Former director of a cohesion institution that bridges academia and practice)At every conference, we have a panel of practitioners and a practitioner speaker. And people like that. (Cofounder of a cohesion institution that bridges academia and practice)What I found interesting about the ""7 Big Problems in Marketing"" work at the AMA is that we were really trying to get at which problems practitioners today are facing, or that we see coming,...and those were defined kind of collectively between academics and practitioners. (Former global chief marketing officer of a large multinational technology corporation)In my last trip [to an MSI meeting], in San Francisco right before COVID-19, there was a cocktail [hour] where we had various academics explaining their research with whiteboards; we could walk out and talk about their research, et cetera....It was really interesting. (Current EVP at one of the world's largest market research firms)Third, nearly all external stakeholders expressed a more negative view on the extent to which business school faculty is achieving q-quality in research (vs. the extent to which it is achieving r-quality in research), consistent with the insights we obtained from interviewing the (associate) deans:[Academic research in business schools] feels like a small set of people speaking to each other about something that nobody cares about. I may be a little harsh here, but it is often not applicable to the kind of problems I see. (Current senior executive at a cohesion institution that bridges academia and practice)I think there is a stereotype we have on our side is that academic research is ""out of touch"" with reality. (Current EVP at one of the world's largest market research firms)Academic research is highly differentiating but not necessarily as relevant....And obviously, the two things are easily at odds....If you are highly relevant, you are not ""different."" And I think that's the challenge. Does academic research want to be more relevant? Or does it want to maintain its differentiation? Because while it clearly is rigorous, it is largely unassailable, I would say, to the business community. (Current CEO at a leading market research firm in the United States)Most of my peers in the business functions [marketing, strategy, and corporate reputation] would only look at academic research if it was sort of quoted in the context of another business story. Our analytics folks will definitely go deeper into academic papers, specifically if it's helping them. (Current CEO at a leading market research firm in the United States) Discussion: Implications and Limitations ImplicationsOur results have three main implications for business schools and the research faculty they employ. In these implications, we embed several conjectures that can provide fertile ground for future research to provide empirical testing. Implication 1: business schools need to develop better research metricsResearch monitoring instruments in business schools are, on average, badly designed. Low-effort metrics, such as the number of publications, receive too much weight in faculty evaluation, whereas effortful metrics such as creativity, literacy, relevance to nonacademic audiences, and awards receive too little weight. Business schools with badly designed monitoring instruments perform worse on (r- and q-) quality of research than business schools with well-designed monitoring instruments. Business schools need to develop better research metrics. Business schools that take this message to heart could consider multiple pathways.First, business schools could devote more effort to otherwise low-effort metrics to make them more informative. For instance, schools can correct aggregate publication counts for journal status. Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research, and Marketing Science are journals that publish, on average, higher-quality articles than other journals in marketing (according to the UTD list, which is the most stringent list on quality). Alternatively, schools could correct aggregate citation counts for ( 1) whether a scholar's highly cited papers were original contributions in premier journals or review articles in secondary journals, ( 2) whether a scholar's articles are consistently in the top 20% cited papers or bottom 20% cited papers of a journal, ( 3) whether a scholar's top five or top ten cited articles were published in premier or secondary journals, and ( 4) whether a scholar's work is mainly cited by papers in premier or secondary journals.Second, business schools could consider low-effort metrics such as the number of publications or citations only as a starting point for faculty evaluation rather than an end point. For instance, for citations, it would be meaningful to rank a professor's work according to Web of Science citations, after which the five highest-ranked articles are assigned for reading to a committee, which assesses the r- and q-quality of the respective five papers after reading them. Ideally, these committees would provide thorough evaluations of the work, rather than a mere summary. One (associate) dean also told us about the practice of assigning discussants on specific papers of a candidate up for a promotion and tenure (P&T) evaluation to stimulate reading and evaluation. Instead of scientometrically picking the best three to five papers for reading, schools could also ask the candidate to pick three to five of their best papers and ensure that evaluators read and discuss those papers.Third, business schools could add creativity and literacy of scholarly work to the evaluation process, piggybacking on recent work enabling their reliable and valid measurement.[20] Business schools could also improve creativity training and coaching of doctoral students and young faculty ([51]). Innovation management as a field has shown that creativity, ideation, idea development, are all processes that can be trained with tools; doctoral students and young faculty could be trained on such tools (for examples of such tools, see www.frisbuss.com).Fourth, business schools could make the system of reference letters used for P&T decisions more effective by ( 1) providing a cohort list to which the candidate should be compared, ( 2) making evaluation criteria such as creativity and literacy explicit, and ( 3) involving a more heterogeneous set of letter writers. To prevent gaming of cohort lists, schools could decide on a universal set of reference schools, such as the 10–20 schools that perform similarly or a little better on the FT overall or UTD research rankings. The cohort for a specific candidate in a P&T process could consist of two types of faculty members of the reference schools: ( 1) all research faculty with a similar ""time since doctoral degree"" (e.g., ±1–2 years) and ( 2) all faculty of the same rank for which the candidate is considered who received their doctorate no more than five years prior to the candidate. To source letter writers, business schools could ( 1) source academic experts from the entire discipline across silos, instead of purely from the silo to which the candidate belongs and ( 2) allow nonacademics (e.g., alumni, students, professionals) to write letters, as we observed in one school we studied where a typical P&T package could have up to 50 letters. Implication 2: business schools need to improve alignment with their faculty on compensationFaculty members feel undercompensated, whereas several (associate) deans feel they are overcompensated for the research they do. Business schools where faculty feel more appropriately compensated perform better on r-quality of research than business schools where faculty feel less appropriately compensated. Business schools that aim to improve the alignment with their faculty on compensation can do so in multiple ways.First, business schools could give faculty a better understanding of the entire organization, its operations, and its finances. Some schools have a well-developed habit of organizing faculty meetings where they transparently cover all aspects of the school's business. In one of the business schools we studied, faculty meetings periodically cover the school's income statement, sales forecasts, and balance sheet to increase faculty's understanding of the economics of the school. Other schools do not share—or purposefully hide—financials, which prohibits the faculty from seeing their salary and contribution in the context of the bigger picture.Second, business schools could showcase what staff, administrators, (associate) deans, and other senior faculty do on a day-to-day basis to improve the school's health. We have seen ""a day in the life of..."" presentations by deans to give faculty a better idea of what kinds of internal and external pressures they are facing. Transparency on such direct contributions to the health of the school may put the research accomplishments of a research faculty member (such as another Journal of Marketing or Journal of Marketing Research publication being freshly accepted) into perspective.Third, business schools could promote teamwork and collaboration among faculty within the same school, fostering a high-commitment environment. Such collaborations may stimulate the faculty's emotional identification with the school. While considering such promotion, schools also need to put checks in place against undesirable practices, such as forcing people into collaborations, free-riding in collaborations, or junior faculty trading in coauthorships for political or teaching support, often from senior faculty, among others.Fourth, business schools could increase the leverage over faculty to ensure that their research faculty meet the outside world also from a compensation perspective. Specifically, we believe that business school professors would benefit from practicing in their professional area just as medical school professors benefit from seeing patients or law professors benefit from assisting in writing and enforcing legislation, practicing law, or performing expert witness services. Outside activity by professors would also give them an outside valuation on their time. Such external valuation could ( 1) bring the compensation demanded from the school more in line with actual valuation by external stakeholders and ( 2) complement the pecuniary reward from the school, lowering the faculty's dependency on the school's paycheck. Implication 3: business schools need to improve the quality (especially q-quality) of their f...Research r-quality is a stronger driver of business school research health than research quantity. Compared with research quantity, research r- and q-quality are stronger drivers of business school health dimensions other than business school research health. Research quantity can even negatively affect external support. The (associate) deans report that the business schools they lead have made more progress on r-quality than on q-quality and that they are concerned about a further decline in q-quality in recent years. This viewpoint is shared by the external stakeholders we interviewed. Business schools that want to improve the r-quality and/or q-quality of their faculty's research can do so in multiple ways.First, business schools could focus audits of their research activities more on quality than on quantity. Business schools that want to increase r-quality could investigate whether their metrics sufficiently reward quality, whether they allocate research money sufficiently based on quality, and whether its faculty is sufficiently represented on the Editorial Review Boards of the best journals in the field. Business schools that want to increase q-quality could investigate whether the school sufficiently stimulates consulting by faculty high in r-quality (as recommended in [43]] and [52]]), whether research centers fundamentally engage with practice or are mostly ""lipstick on a pig"" (as one our interviewed associate deans put it), whether research faculty high in r-quality teach in executive MBA or open and custom programs (which provide more socialization with practice than undergraduate or daytime MBA programs), and whether the portfolio of research faculty profiles is balanced sufficiently both on r-quality and q-quality.[21]Second, business schools could consider complementing internal audits (e.g., of a multidepartment committee chaired by the research dean) with external audits by a panel of outside faculty with outstanding research records, preferably on both r- and q-quality, and with a good understanding of business school health. For schools that have not done a research audit for a while, these findings and suggestions could stimulate them to organize such audits. For schools that already perform such audits regularly, our findings indicate that the aforementioned topics should make such audits more impactful and focused on today's major challenges of business schools.Third, business schools could benchmark their experiences with those of successful business schools, or role models, which can serve as yardsticks for improving their research faculty incentive systems. Role models help clarify an ""aspiration gap"" (i.e., the difference between a level of performance that one aspires to achieve and the level of performance that one already has). Moreover, different business schools have different aspiration levels and thus place different weights across different dimensions of the research task they want to optimize. Thus, each business school should benchmark its faculty research incentive system with that of a weighted combination of other business schools chosen to generate a ""synthetic role model"" that closely resembles the performance that the school aspires on research quantity, r-quality, and q-quality. As an illustration of the usage of these ""synthetic role models,"" we present, in Table 5, three stylized synthetic role models that may serve as inspiration for schools aiming to increase their performance in research quantity (SRM-Qty), r-quality (SRM-R), or q-quality (SRM-Q).GraphTable 5. Synthetic Role Models According to a School's Leading Research Task Optimization Goal. Fourth, faculty could consciously strengthen the cohesion institutions that support the promotion of socialization with practitioners (e.g., AMA, MSI, Theory + Practice in Marketing) and business schools could encourage and support such efforts. Within such cohesion institutions, faculty could stimulate action that increases q-quality of research of high r-quality. For instance, institutions such as MSI could give fewer, larger grants, possibly assigning a corporate sponsor to steer such larger grants, or grant funding only to research teams that combine academics and practitioners. Under its present organizational structure (the senior leadership team being fully composed solely of practitioners), the AMA has failed to make the connection between academics and practitioners (as noted by the representative from the AMA we interviewed). Business school marketing faculty could aid in building a new model within the AMA. LimitationsSeveral limitations of this article may give rise to future research. First, our empirical evidence is self-reported from a survey with research faculty, interviews with (associate) deans and interviews with representatives of external institutions. While self-reports enable us to cover a broad set of topics, each of the relationships we establish could potentially fuel secondary data research. Several secondary data studies (e.g., [34]; [39]) have examined the effect of research on teaching, but none have examined the effect of research on other business school health dimensions, such as external support or institutional integrity, all of which could be gauged by secondary data also (e.g., endowment statistics, online chatter of student communities). Future research should also better examine other constituents' perceptions of business school health (e.g., students, recruiters, donors, alumni).Second, our conceptual derivation and empirical evidence only limitedly exposes the causal mechanisms at work. In fact, we have been prudent throughout the article to clearly identify instances where our data permits us only to offer logical conjectures and to claim correlation rather than causation. Thus, future research that goes from correlation to causation would be very fruitful; it could also document more precisely the nature of the feedback mechanisms that we introduced. Future research could also more elaborately document the behavioral mechanisms in place that lead business schools to excessively monitor numbers and insufficiently monitor creativity or literacy. One can conceive behavioral experiments with academic assessors on research metrics, how people use them, and under which conditions decisions can be (de)biased.Third, we explored the variance in incentive misalignment across schools on a limited number of school descriptors. Research could easily expand on a larger set of school descriptors. For instance, do the effects we study depend on whether the school offers executive education, where the school is located (United States vs. international), whether the school is private or public, or how high the tuition fees are that it is charging?Fourth, we took a step beyond our empirical inquiry to conceptualize what business schools could do to positively affect the present state of affairs. Some of the recommendations we gave seem easy to implement, whereas others are more difficult and would benefit from a more elaborate conceptualization than the length and scope of this article allow. For instance, how can business schools create a stronger sense of common purpose among its faculty such that the faculty is less self-interest seeking? Alternatively, how can business schools favor more reading and less counting? How can they better monitor creativity and literacy? The latter question can also fuel scientometric research to address some of the alternative metrics we suggest.Despite these limitations, we feel that we have made a significant contribution to understanding the role of faculty research in business school health. At the very least, we hope that we have sparked a dialogue to get more (marketing) faculty and business school administrators to rethink how academic research can make business schools healthier. " 22,Fields of Gold: Scraping Web Data for Marketing Insights," Marketing scholars increasingly use web scraping and application programming interfaces (APIs) to collect data from the internet. Yet, despite the widespread use of such web data, the idiosyncratic and sometimes insidious challenges in its collection have received limited attention. How can researchers ensure that the data sets generated via web scraping and APIs are valid? While existing resources emphasize technical details of extracting web data, the authors propose a novel methodological framework focused on enhancing its validity. In particular, the framework highlights how addressing validity concerns requires the joint consideration of idiosyncratic technical and legal/ethical questions along the three stages of collecting web data: selecting data sources, designing the data collection, and extracting the data. The authors further review more than 300 articles using web data published in the top five marketing journals and offer a typology of how web data have advanced marketing thought. The article concludes with directions for future research to identify promising web data sources and embrace novel approaches for using web data to capture and describe evolving marketplace realities.","The accelerating digitization of social and commercial life has created an unprecedented number of digital traces of consumer and firm behavior. Every minute, users worldwide conduct 5.7 million searches on Google, make 6 million commercial transactions, and share 65,000 photos on Instagram ([76]). The resulting web data—enormous in size, diverse in form, and often publicly accessible on the internet—is a potential goldmine for marketing scholars who want to quantify consumption, gain insights on firm behavior, and track social activities difficult or costly to observe otherwise. The importance of web data for marketing research is reflected in a growing number of impactful publications across all methodological traditions, including consumer culture theory, consumer psychology, empirical modeling, and marketing strategy.Researchers can use web scraping and application programming interfaces (APIs) to efficiently collect web data at scale. Web scraping is the process of developing software to automatically collect information displayed in a web browser. For example, researchers can scrape Amazon's website to construct data sets of online consumer reviews. Because many websites and web apps are publicly accessible, data sets can be generated without involving data providers. In contrast, some data providers also offer APIs for programmatic access to their internal databases. For example, scholars can apply for academic research access to retrieve data from the Twitter API. Researchers can also access a wide range of algorithms via APIs. For instance, Google offers advanced image and video recognition through its Cloud Vision API (for additional examples and explanations, see Table W1 in Web Appendix A).Data extracted from the internet, at first sight, might resemble other organically generated data sets that address related research questions (e.g., a firm's clickstream data). Yet, collecting web data for academic use in a highly automated manner may prompt a set of novel and sometimes insidious validity challenges. Validity concerns may arise from, among others, ( 1) failing to capture contextual information in a rapidly changing environment (e.g., updates to the website's data-generating process), ( 2) not sufficiently aligning the psychological processes of interest with the frequency of data extraction on review platforms (e.g., the collected information does not capture the time when the behavior occurred), ( 3) overlooking the influence of algorithmic interference on e-commerce websites (e.g., the effect of personalization algorithms on information display), or ( 4) failing to retain raw website or API data necessary for construct validation, sampling, and analysis.Against this background, this article makes three interlinked contributions. First, we develop a methodological framework that highlights how addressing validity concerns arising from web scraping and APIs requires the joint consideration of idiosyncratic technical and legal/ethical concerns. Within marketing, guidance exists for collecting web data in the consumer culture theory research tradition, particularly using netnography (e.g., [46], [47]). A handful of articles address selected challenges that occur during the automatic extraction of web data (e.g., sampling; [39]). Outside of marketing, tutorials and books primarily focus on technical details for the automatic extraction of web data (see Table W2 in Web Appendix B). Yet, neither these resources nor methodological articles in other disciplines (e.g., [19]; [52]) address the broad spectrum of validity concerns arising from the automatic collection of web data for academic use. It is this void that our methodological framework fills. In discussing the methodological framework, we offer a stylized marketing example for illustration and provide recommendations for addressing challenges researchers encounter during the collection of web data via web scraping and APIs.Second, despite the use of web data in marketing for two decades, no systematic review reflects on how it has and could advance marketing thought. Importantly, understanding the richness and versatility of web data is invaluable for scholars curious about integrating it into their research programs. To offer these insights, we have systematically reviewed more than 300 articles in the top five marketing journals across two decades that have used web data. We leverage our coding to reveal which web sources have been considered and how data have been extracted. The resulting typology of web data may spark the imagination of researchers interested in generating new marketing insights from web data.Finally, we use our methodological framework and typology to unearth new and underexploited ""fields of gold"" associated with web data. We seek to demystify the use of web scraping and APIs and thereby facilitate broader adoption of web data across the marketing discipline. Our future research section highlights novel and creative avenues of using web data that include exploring underutilized sources, creating rich multisource data sets, and fully exploiting the potential of APIs beyond data extraction. We particularly highlight the value of web scraping and APIs for research streams that have not yet embraced them at scale.In what follows, we provide an overview of the use of web data in marketing and document four pathways through which web data have advanced marketing thought. We then introduce our methodological framework to help researchers make sensible design decisions when automatically extracting web data. We conclude with directions for future research. Using Web Data to Advance Marketing ThoughtAcross the top five marketing journals, marketing researchers increasingly use information available on the internet. For example, the share of web data–based publications has more than tripled in the last decade, from about 4% in 2010 to 15% in 2020 (see the thick line in Figure 1). The growing use of web data has been fueled by its increased accessibility and associated time and cost savings. Most of the 313 identified web data–based articles rely on web scraping (59%); APIs are used much more sparingly (12%), and some articles combine web scraping and APIs (9%). The remaining articles—especially netnographic work—use web data but tend to extract it manually (20%). The median annual citation count of articles using web data is 7.55, compared with 3.90 for publications not using web data.Graph: Figure 1. Increased use of web data in marketing (2001–2020).Some of the earliest uses of web data in marketing can be attributed to the development of netnography to study online communities (e.g., [46], [45]). Subsequently, the first quantitative marketing scholars extracted web data at scale (e.g., [27]). Today, all subfields—including marketing strategy and consumer behavior—have embraced web data.Online word of mouth and social media are the most prominent domains of inquiry using web scraping (see Table W3 in Web Appendix C). The most widely used data source in academic marketing research is Amazon (38 articles). Other prevalent sources are Twitter (30), IMDb (24), Facebook, and Google Trends (both 22; see Table W4 in Web Appendix C).Via a comprehensive literature review, we next identify the four central pathways through which web data facilitate the creation of new knowledge in marketing. Studying New PhenomenaWeb data can boost the field's relevance by enabling marketing scholars to study novel phenomena. For example, initial work using web data focused on novel online phenomena that emerged at the beginning of this century, such as online conversations ([27]) and the impact of consumer reviews on sales ([14]). Web data are well suited to provide fertile grounds for inductive research to develop novel theories about emerging marketing phenomena (e.g., brand public; [ 4]).Gathering data via web scraping or APIs often decreases the time between the occurrence of a marketplace phenomenon and the availability of data for academic research. This inherent timeliness of web data continues to be an essential lever for marketing scholars to advance our understanding of emerging substantive topics such as the sharing economy (e.g., Airbnb; [93]), access-based business models (e.g., Spotify; [15]), and fake online content (e.g., [ 3]). More generally, web data enable researchers to weigh in on contemporary issues before any ""conventional"" data sets become available, such as measuring the effect of pandemic lockdown policies on consumption ([74]). Boosting Ecological ValueWeb data can create knowledge by allowing researchers to move closer to marketing's ""natural habitat"" ([83]). Some of the most used web sources contain commercial outcome variables relevant to marketing stakeholders and are difficult or costly to collect otherwise. Examples are sales (e.g., The-Numbers.com), sales ranks (e.g., Amazon), online searches (e.g., Google Trends), and donations (e.g., contributions to a Kiva project).As web data can be collected unobtrusively, they can effectively complement more controlled data collection methods. Using web data, researchers can demonstrate that focal psychological processes occur outside the confines of a controlled laboratory environment and stylized experimental stimuli ([64]). Consider, for instance, the controversy around the decoy effect ([37])—one of the most prominent context effects in consumer behavior. Using experiments, [23] questioned the robustness and practical relevance of the decoy effect. In response, [90] built a panel data set from an online diamond market using web data. Their work not only shows that the decoy effect emerges in a high-stakes setting but also, more importantly, reaffirms its practical significance by quantifying its profit implications for the diamond retailer.Another benefit of using web data to boost ecological value is that they can often be collected without the data provider's direct involvement. Thereby, researchers can limit the interference of data suppliers or collaborating firms to ensure that the societal relevance of a particular research question is given precedence over business objectives (e.g., firms might be unwilling to share data about the tracking tools they use on websites; [81]). Further, using web data, researchers can ensure the publication of research findings, regardless of how palatable they are to the organizations that are being studied. Facilitating Methodological AdvancementAs much of the data produced by consumers and firms is inherently unstructured, extracting insights can be challenging ([88]). Thus, marketing researchers have leveraged web data for developing methods that deal with and extract insights from different types of unstructured data, such as textual, image, and video data. For instance, web data have fueled the rapid improvement of automated text analysis (see [ 6]) and the large-scale analysis of image and video content (e.g., [55]; [57]).The availability of network data on the internet (e.g., friend or product networks), along with outcome variables (e.g., posts, likes, sales ranks), has further enabled the use and advancement of methods for analyzing networks (e.g., [67]). Given their wealth and richness, web data have also stimulated the development of novel methods that can complement or replace traditional marketing research methods (e.g., using user-generated content to construct accurate multidimensional scaling maps of brands; [65]). Improving MeasurementWeb data can advance marketing knowledge by allowing researchers to measure constructs more precisely and obtain more valid inferences. For example, the collection of adequate control variables is often difficult. To capture seasonality in purchase patterns across a wide range of geographical markets and calendar years, researchers have used APIs to construct continuous (vs. dichotomous) variables that accurately reflect national holidays (e.g., HolidayAPI; [16]). Web data also allow researchers to efficiently operationalize new measures at scale, such as weather conditions based on the location of users' IP addresses (e.g., Weather Underground; [53]).Relative to non-web data sources, researchers can collect data on the behavior of many consumers and firms at higher frequencies ([ 1]). Such data enhance statistical power, enable identification of causal effects, and facilitate the examination of theoretically relevant variation within individuals over time (e.g., how various psychological distances shape review content for the same consumer; [35]) or how effects unfold over time (e.g., the impact of video elements on virality over time; [77]). SummaryTable 1 presents a typology of the four central pathways through which web data have advanced marketing thought. The typology highlights web data-based studies that investigate key marketing constructs across different entities, from consumers to organizations and other marketing stakeholders. For example, [80] explored a new phenomenon (tweeting), focusing on consumers (i.e., their motivation to tweet). These pathways for knowledge creation from web data are not mutually exclusive. Combining different pathways might be particularly promising for making breakthrough contributions.GraphTable 1. How to Create Knowledge Using Web Data: A Typology. 1 Notes: The table highlights illustrative and diverse examples of web data–based studies and corresponding outcome variables, cross-tabulated by four pathways through which web data have advanced marketing thought (the columns) and three of the most studied actors in marketing research (the rows).Next, we introduce our methodological framework, which outlines an approach for making design decisions that enhance the validity of web data collected via web scraping and APIs. Researchers interested in learning more about the technical details of automatically extracting web data can consult our curation of technical tutorials in Web Appendix B or the digital companion to this article (available at https://web-scraping.org), which features a searchable database of all marketing articles in the top five marketing journals using web data. Methodological Framework for Collecting Web DataIn automatically collecting web data using web scraping and APIs, researchers make seemingly innocuous design decisions. However, as we will show, these decisions often involve trade-offs about research validity, technical feasibility, and legal/ethical risks[ 5] that are not always apparent. How researchers resolve these trade-offs shapes the credibility of research findings by enhancing or undermining statistical conclusion validity, internal validity, construct validity, and external validity ([73]).We develop a methodological framework to provide guidance for the automatic collection of web data using web scraping and APIs. Figure 2 offers a stylized view of this process involving three key stages—source selection, collection design, and data extraction. Researchers typically start with a broad set of potential data sources and eliminate some of them as a function of three key considerations—validity, technical feasibility, and legal/ethical risks. These three considerations appear in the corners of an inverted pyramid, with validity at the bottom to underscore its importance. Given the difficulty in projecting the exact characteristics of the final data set before it is collected, researchers often revisit these considerations as they design, prototype, and refine their data collection. Failure to resolve technical or legal/ethical issues might mean that web data cannot inform the research question meaningfully.Graph: Figure 2. Methodological framework for collecting web data.Our framework deliberately focuses on collecting web data rather than its subsequent analysis. Analyzing web data involves many familiar methodological challenges encountered with organically generated data (e.g., cleaning to remove erroneous data or create measures, selecting observations, addressing endogeneity). However, approaches for the valid collection of web data are not yet documented nor commonplace in marketing research.The methodological framework—designed to guide the automatic extraction of web data at scale—is agnostic to research paradigms. It is applicable to both deductive (i.e., identifying compelling web data to test hypotheses) and inductive (i.e., observing interesting irregularities in web data to identify novel marketing concepts and/or novel relationships between constructs) approaches to theory building. We next highlight the idiosyncratic challenges encountered when collecting web data and summarize solutions to these challenges in Tables 2–4. For expositional clarity, we focus on web scraping in our text.[ 6] To illustrate the key challenges encountered in designing the data collection, we gradually introduce a stylized marketing example involving the collection of book reviews from Amazon.GraphTable 2. Challenges and Solutions in Selecting Web Data Sources. GraphTable 3. Challenges and Solutions in Designing Web Data Collections. GraphTable 4. Challenges and Solutions in Extracting Web Data. Data Source SelectionA critical first step in the use of web data is selecting the data source(s). We examine three challenges faced by researchers in this selection process. First, it is essential that researchers explore the universe of potential sources (challenge #1.1). Second, researchers need to consider the range of possible extraction methods (challenge #1.2). Third, it is crucial to map the context in which the data are generated (challenge #1.3). Table 2 summarizes our recommendations for tackling these challenges. Exploring the Universe of Potential Sources (Challenge #1.1)In the absence of conventional gatekeepers (e.g., data providers), researchers can select from countless web data sources. For example, there are 2.1 million online retailers in the United States alone ([20]). Further, websites and APIs differ greatly in scope (e.g., number of users), data quality (e.g., consistency), and retrievability (e.g., extraction limits). Even within the same product category, data sources differ vastly. For example, Amazon reports a book's sales rank (an aggregate outcome metric for product sales), whereas Goodreads reports users' reading behavior (an individual outcome metric for consumers' usage intensity).Faced with a vast universe of potential sources, researchers may be tempted to focus on familiar platforms only ([89]). For instance, Amazon is the most used web data source in marketing (see Table W4 in Web Appendix C). Amazon might be a relevant source to extract book reviews, given its broad assortment and user base. Yet, in other cases, researchers might miss opportunities for identifying novel, emerging marketing phenomena or conduct more compelling theory testing without a thorough exploration of potential sources. Researchers can avoid the pitfalls of defaulting to dominant sources by actively considering a broad spectrum of websites and APIs, ranging from highly popular (e.g., Amazon) to less popular sources (e.g., Goodreads), from primary data providers (e.g., YouTube) to data aggregators (e.g., Social Blade), and from platforms with global reach (e.g., Twitter) to more regional ones (e.g., Taringa!). Another strategy to move beyond familiar sources is to adopt alternative perspectives. For instance, researchers can consider websites or APIs that are used by consumers, analysts, or managers. API directories at GitHub or programmableweb.com can facilitate identifying potentially relevant APIs.A broad exploration of web data sources may lead researchers to discover sources that may be more permissive for (academic) data extraction or less likely to trigger ethical concerns. For example, websites that do not require logging into the site to reveal information are typically more scraping-friendly than sites that first require registering a user account. In the case of Amazon, researchers can obtain most information without logging in and do not have to explicitly provide their agreement to the website's Terms of Service. To reduce legal (e.g., breaches of contract, as researchers have provided explicit agreements to the terms of service) and ethical (e.g., website users may consider their data private) risks, researchers should refrain from creating fake accounts to access information requiring a login. By explicitly declaring their academic status (e.g., when registering at the site using the institutional email address), researchers might be able to diminish their exposure to legal risk.When exploring web sources, researchers need to examine whether theoretical constructs can be operationalized in a valid manner ([91]). A healthy level of skepticism is warranted when using idiosyncratic metrics from APIs or websites. For example, researchers might be interested in scraping the price tier of restaurants from Yelp. Yet, it is not entirely clear how Yelp computes this metric from individual consumer ratings.To determine when to stop exploring sources, researchers need to assess to what extent the selected source(s) improve(s) on alternatives. One way to justify selecting a single web source is the presence of unique features. For example, a researcher studying how observers react to humor in reviews might prefer Yelp to alternative platforms as it is the only source featuring ""funny"" votes (e.g., [60]). At other times, researchers may be indifferent between potential sources and can draw from multiple sources to boost the generalizability of their findings (e.g., tweets and restaurant reviews; [61]). Collecting data from multiple sources is often useful because even similar types of information (e.g., consumer comments) may affect marketing outcomes differently, depending on source characteristics (e.g., forums vs. microblogs; [72]). Data aggregators—some of which offer authorized data access via APIs—facilitate the collection of such multisource data. Considering Alternatives to Web Scraping (Challenge #1.2)The popularity of web scraping may lead to the conclusion that it should be preferred over other methods. However, some web sources offer access to data via APIs ([12]). In general, extracting data via APIs is more scalable and less likely to invoke the same level of legal risks compared with web scraping. Although some sources offer unconstrained APIs that do not require authentication, others require (paid) subscriptions and authentication procedures. Some sources, such as Twitter, have recently started offering APIs for academic research. In the case of Amazon, an API offering access to consumer reviews is currently not available.In addition to APIs, numerous other options exist for researchers to obtain web data. For example, some data providers (e.g., Yelp, IMDb), public data platforms (e.g., Kaggle, The Dataverse Project), and researchers (e.g., [59]) provide documented web data sets that can readily be used for academic research. There are many potential use cases of such data sets, but less than 5% of all web data–based articles in marketing used such data sets. To avoid the pitfall of defaulting to web scraping for data extraction, researchers can expand their search by explicitly including terms such as ""API"" or ""data set download"" in their search queries. Mapping the Data Context (Challenge #1.3)Relative to other frequently used archival sources in marketing, web data entail large and often undocumented complexities. Thus, it is critical that researchers map the data context, which involves identifying relevant contextual developments that may undermine the validity of the research if gone unnoticed.First, mapping the data context may reveal changes in the underlying data structure. For example, a major change in a platform's user interface may affect subsequent consumer behavior. Second, mapping the data context enables researchers to identify relevant pieces of information for collection together with the focal web data. For example, researchers may discover an external website (e.g., Statista) that offers information about the composition of a focal data source's user base. If stored, such data could eventually be used to detect changes in the composition of the user base or verify the representativeness of the extracted data. Third, mapping the data context may reveal unknown information, potentially allowing researchers to discover novel research opportunities. For example, researchers may use the (unexpected) launch of a new recommendation system at a music streaming service as a natural experiment to investigate the impact of recommendations on music consumption.To understand and map the contextual complexity of web data, researchers can immerse themselves in the ecosystem surrounding the focal source by signing up and using the source, tracking press releases, social media, and scanning the competitive environment. Helpful tools include a search engine's advanced search features, newsletters, and alerts on leading business and technology magazines (e.g., TechCrunch.com, WSJ.com, FT.com). The website's source code may also hold valuable information about potentially relevant environmental changes. Sometimes, researchers may also detect the presence of algorithms on the site that may threaten the validity of the collected data. For example, Amazon's product pages personalize information based on which preceding products were viewed—even without users logging into the site. Designing the Data CollectionAfter narrowing down potential sources, researchers decide which information to extract from them (challenge #2.1), how to sample (challenge #2.2), at which frequency to extract the information (challenge #2.3), and how to process the information during the collection (challenge #2.4). Table 3 summarizes these challenges and corresponding solutions. Which Information to Extract? (Challenge #2.1)In the absence of any ""downloadable"" data set, the first challenge lies in deciding which information to extract from a source. Researchers begin by browsing the web page to identify from which pages to extract which information. In our Amazon example, some of the most commonly used pages are product pages (e.g., [14]) and review pages (e.g., [84]). Generally, pages such as those from e-commerce platforms contain information from the company's database, offering researchers the opportunity to capture some of the information available at a company. Collecting such data involves iterating through a set of related pages (i.e., browsing through many product pages and corresponding review pages in our Amazon example) and saving the data as it becomes visible.As the goal of websites like Amazon is rarely the provision of data sets for academic research, it is often necessary to combine information from different pages (e.g., book descriptions from the product page and ratings from the review page). It is particularly difficult to recognize the subtleties of available information, which makes the decision from which pages to extract information challenging. For example, researchers interested in building a data set of book reviews would find total ratings both on the product and review page, but only the review page reveals all product reviews.[ 7] Yet, neither the product nor the review pages contain all the biographical information available on a reviewer's profile page. Widely exploring a website or API is necessary for identifying information relevant for subsequent analysis (e.g., construct operationalization). The amount and type of information also often vary (e.g., depending on screen width or whether the user is logged in). In this phase, researchers should assess the degree to which the information could be considered personal or sensitive under different regulatory regimes (e.g., the European Union's General Data Protection Regulation [GDPR]), which may require planning measures such as pseudo-anonymization of reviewer names. Researchers may also reassess whether all information needs to be captured to meet the research objective. Suppose reviewer names are strictly necessary (e.g., because they allow for matching different sources). In that case, researchers can explore whether the targeted web data source offers ways to exclude subjects governed by prohibitive privacy regulations (e.g., by using filters).An important threat to internal validity in any study involving web data is algorithmic interference (e.g., [91]). The (visual) design of websites that facilitates usability can undermine the validity of the collected data if gone unnoticed and unaddressed. Especially when deciding which information to extract, it is important to reexamine the website or API for the presence of algorithms. For example, the order in which the researchers in our example visited the website while designing their data extraction could affect which related books are displayed on the product pages on Amazon. Other algorithms that often affect the display of data on websites are sorting algorithms (e.g., by popularity or mixed with sponsored search results) and filtering algorithms (e.g., showing subsets of the data). Algorithmic interference is often hard to detect without being sensitive to it. To account for potential algorithmic interference, the researcher might extract variables as part of an algorithm's more extensive set of input variables, which offers opportunities to control for them in the empirical analysis (e.g., the order in which books were extracted in our Amazon example).Researchers also need to establish the intertemporal stability of available information. Because the web is constantly evolving, the information on a page might not have been generated via the same process over time, undermining the internal validity of the data. Some changes to sources are drastic enough to alter how the data were created in the first place, introducing measurement error ([87]). For example, Amazon shifted to a positive-only evaluation of reviews by removing the ""not helpful"" vote button in 2018, and it no longer displays ""not helpful"" counts next to reviews ([28]). This change might have impacted review content (e.g., users writing shorter review texts). Yet, researchers collecting data today cannot find any traces of these ""not helpful"" votes. A tool for examining changes to relevant information on a website is the Wayback Machine (archive.org). Researchers can use this tool to retroactively inspect websites over time (e.g., [58]) or submit their own website links for archiving.Finally, collecting metadata that ""annotates"" the data collection enhances internal and external validity (e.g., storing the timestamp of data extraction, whether an API request was completed successfully, or the IP address from which the data request was made). Such metadata can be used not only for diagnostic purposes but also to link the extracted web data to other data sets. For example, in our Amazon example, the collected data could be linked to other data using IP-based geolocations (e.g., linking geolocation and web search data; [86]) or timestamps (e.g., linking reviews to stock prices; [78]). How to Sample? (Challenge #2.2)A second challenge in designing the data extraction lies in deciding how to sample from the data source. In particular, in the absence of access to the data source's entire database, it is difficult or impossible to draw a random sample from the population (e.g., all products) available at the data source. Instead, researchers need to devise their own sampling frame to reveal the units they want to sample from the website ([66]). For example, researchers could scan the site for an index of all products that could inform their sampling. In our example studying reviews at Amazon, multiple such indexes may be available. Should products be sampled from the bestseller page for books (so-called exposure-based populations; [66]) or instead from the category page for books (i.e., availability-based populations)? Choices like this result in different data and may even invalidate inferences, as sampling frames might inadvertently induce systematic bias ([39]).One common validity challenge in choosing how to sample is determining how many units (e.g., books) are sufficient to inform the research question. From a validity standpoint, it would be ideal to collect information on the entire population (e.g., all books available at Amazon). However, Amazon does not have an obvious page to extract all books. Imagine that a research team wanted to collect information about all marketing books sold on Amazon. The bestseller page, for example, lists only the top 100 bestsellers. By manually changing pagination parameters in the URL, the top 400 bestsellers can be revealed. Yet, this list of 400 books neither constitutes the entire population nor represents a random sample of marketing books sold at Amazon. Alternatively, when starting from the product overview pages, these pages list an imprecise number of books (e.g., ""over 60,000""), which can only be viewed up to page 50. With each result page featuring 24 organic search results, this approach would produce 1,600 books per category at best. Thus, researchers need to consider other ways to identify more books on Amazon, such as searching for books using various keywords. To expand the number of sampled units, researchers could collect data multiple times, use other keywords, or tweak search parameters to reveal more data by requesting narrower subsets from the database (e.g., only books published during a specific month).Even if a list of the population (e.g., all books) could be retrieved, it may be infeasible to extract data within a reasonable time frame. While sample size requirements are mostly concerned with a researcher's inferential goals (e.g., [50]), few articles make the resource constraints that affect collecting web data explicit (e.g., [68]). For example, with web data, a study's sample size critically depends on technical parameters such as the number of computers used for data extraction or the number of pages that need to be visited. We illustrate how to calculate the technically feasible sample size in Web Appendix F, which may effectively complement traditional sample size calculations commonplace in marketing.As a result of these complications, researchers often restrict their sample size. One way to motivate a compelling sampling frame is to use external sources that can be linked to the web data. For instance, the New York Times or Publishers Weekly bestseller lists might be a starting point for sampling books ([14]). An alternative approach focuses on internal data available at the source itself. Researchers may have to allocate substantial time to identify ways to sample from the focal source. Sometimes, starting the data collection from a page unrelated to the focal pages of interest might facilitate collecting a more representative sample (e.g., by reducing geographical biases; [86]). For example, on Amazon, researchers could first sample reviewers and associated demographic information (available at the user profile of reviewers) and subsequently retrieve data on all reviewed products. Similar to how researchers build network data from an initial set of products or users, the sampling units retrieved from an initial set of pages can be considered seeds. In choosing seeds, researchers should be cautious about drawing from vulnerable populations (e.g., minors) or infringing on prohibitive privacy regulations. At Which Frequency to Extract Information? (Challenge #2.3)Web data are nonstatic, as they change often or might disappear altogether. Therefore, researchers need to consider at which frequency to extract information. This decision encompasses whether to collect data once or multiple times and when to run (and potentially schedule) the data extraction. Consideration of the frequency and schedule is challenging but required to ensure the intertemporal stability of measurement, which is critical for internal and construct validity.From a technical and legal perspective, it is most desirable to extract data only once. Single extractions are less likely to represent a burden on the firm's servers, and the extracted data often only represent a limited snapshot of the entire database, reducing the risks of copyright infringement. Further, such data may be more likely to respect users' ""right to be forgotten,"" which is part of the privacy laws in some jurisdictions. Yet, single data extraction might raise several validity issues that can easily go unnoticed. For instance, in our example, researchers extracting book reviews once from Amazon will not be able to identify whether any of the archival information has changed. Only when extracting data multiple times can researchers systematically notice changes on the site, which may lead to the identification of ""fake"" reviews that have been removed by the platform (e.g., [29]). More generally, researchers can compare information over time to detect whether data that initially appeared to be archival is truly archival (i.e., does not change over time).Another concern is that a single extraction may not produce a data set that adequately maps onto the focal processes of interest. For example, suppose researchers in our example want to examine whether a review by a so-called ""Top 1000 Reviewer"" leads to more subsequent reviews from other users. However, the researcher merely observes that the reviewer is a top reviewer at the time of data extraction. This does not necessarily imply that this user had the same status when the review was first posted and thus was most likely to affect subsequent reviewing behavior of other users. Formulating and testing the essential assumptions about the data, including the relation between the time of data extraction and the focal (psychological) processes, is thus critical. The formulation of such assumptions is called a ""data source theory"" ([52]). Testing and refining the data source theory helps take proactive steps to enhance internal and construct validity. In the preceding example, it would thus be necessary to collect data from these review pages closer to the original posting date, ensuring that reviewers classified as ""Top Reviewers"" had that status when their reviews became visible.When extracting data more than once, automatic scheduling can help ensure consistency and contribute to validity. Scheduling is beneficial if the required information is only available in real-time. For example, sales ranks at Amazon are updated hourly for popular products, and historical sales ranks cannot be retrieved. Suppose researchers in our example were interested in studying the sales performance of books over time. In that case, they could repeatedly extract the books' sales ranks from the product pages at Amazon. Sometimes fixed intervals enhance validity (e.g., every Monday, 8 a.m.). In other circumstances (e.g., when collecting data from many pages), it may be better to vary the starting time or weekday of the data extraction.Another decision is whether to set an end date for the data extraction. Collecting data over extended periods offers the potential for researchers to build a programmatic stream of research and stumble into unexpected natural experiments (e.g., [13]). Especially for longitudinal data collections, continuing the data collection while the project is in the review process brings numerous benefits, such as the ability to update the data (e.g., a longer time frame, new measures). Yet, concerns about technical feasibility (e.g., storage requirements, continued availability of data source) grow as the data extraction horizon extends. Similarly, from an ethical perspective, the longer the data extraction, the greater the likelihood of potentially identifying individuals via triangulation. Next to ethics, long-term data collection also places a heavier load on servers, potentially increasing exposure to legal risks. How to Process the Information During the Extraction? (Challenge #2.4)As a final step in designing the data extraction, researchers decide how to process the information while it is collected. Any kind of web data collection requires a minimal degree of processing, given that the information is available in a computer's memory (e.g., in the browser or the software processing the API output) and still needs to be stored in files or databases. Thus, this processing step occurs before data sets are cleaned or analyzed.When deciding on how to process information during the extraction, researchers must balance potential efficiency gains from molding raw web data into readily usable data sets with the potential threats to validity due to ""on-the-fly"" processing. For example, in our Amazon example, researchers may be tempted to remove seemingly unnecessary information (e.g., image links in reviews), apply text processing (e.g., removing characters used as separators), or force specific information (e.g., prices) to be stored in a strictly numeric format. Such on-the-fly processing promises to produce essential efficiency gains, as the data set resulting from the extraction could directly be analyzed. However, because on-the-fly processing decisions are usually made after the inspection of only a limited number of pages in early prototypes of the data collection, it is difficult to guarantee their correctness. For example, using our example, what if the initial screening revealed only pictures posted in a review, while the extensive data collection revealed the need to capture video files? Given this and related challenges, keeping the raw data (such as the source code of websites, API output, or any media files loaded at the time of data extraction) is ideal from a validity perspective. For example, even if the data collection breaks, researchers could still process and use the information after debugging their extraction code. Retaining the raw data can also help reduce Type 1 errors by increasing transparency about researchers' degree of freedom in collecting and processing the web data. Yet, retaining the raw data prompts significant concerns about the technical feasibility and ethical risks. From a technical standpoint, storing the raw data might require databases to retain their original structure and facilitate processing, especially for projects involving many raw data files collected over extended periods. Keeping all raw data might raise questions regarding the right to store the raw data—especially if it is not (pseudo-) anonymized before storage.Finally, retaining the raw data allows researchers to refine their extraction design at later project stages. For example, a researcher might have collected Amazon reviews in 2018—around the time of the removal of the ""not helpful"" voting feature. Although extracting ""not helpful"" votes was not part of the original extraction design, researchers would be able to use the raw web data to examine the effect of the removal of these ""not helpful"" votes. Collecting the DataAfter source selection and designing the data collection, researchers gradually transition to turning their small-scale prototype into stable extraction software. In so doing, researchers face three challenges. First, researchers may need to improve the performance of their extraction software when operating it automatically at scale (challenge #3.1). Second, they may need to implement monitoring checks to be alerted to any issues arising during extended data collections (challenge #3.2). Third, researchers should compile information important for documenting the final data set (challenge #3.3). Table 4 contains a summary of solutions and best practices to these challenges. How to Improve the Performance of the Data Extraction? (Challenge #3.1)In scaling up their data extraction, researchers may notice that the extraction software frequently breaks across a larger number of pages or runs significantly slower than expected. Such technical challenges, if unaddressed, have the potential to undermine research validity (e.g., missing data, not meeting sample size requirements). A practical solution to preempt these and similar challenges involves capturing the focal information in different ways and storing raw data—especially in the early stages of data collection and for more ambitious, large-scale web data collection projects. To track whether the extraction targets are met, researchers can log the (timestamped) URLs of scraped pages and visualize the performance of the extraction software over an extended period. The resulting ""effective"" extraction frequency can then be used in recomputing the technically feasible sample size (see Web Appendix F). Novel web scraping services promise to handle technical difficulties efficiently (e.g., ScrapingBee, Zyte). How to Monitor Data Quality During the Extraction? (Challenge #3.2)As a next step, researchers consider which metadata can help them diagnose issues with the data collection in real-time. Especially when websites constantly change, monitoring the health of web scrapers can be a tedious task. Researchers should consider performance at a higher level (e.g., the file sizes of extracted raw data) and lower level (i.e., the accuracy of the information in resulting data files) to assess whether the collection is proceeding as expected. When collecting over long periods, automatic reporting can greatly facilitate monitoring. Finally, alerts (e.g., via email or mobile) can help researchers detect predefined data issues quickly. How to Document the Data During and After the Extraction? (Challenge #3.3)During the data extraction, researchers need to record relevant information about the data in real-time. This is an essential step in building documentation, enabling future data usage by the researcher(s) who collected the data and other scholars. Even after the data extraction has ended, researchers can continuously refine the documentation as they become familiar with the characteristics of the data (e.g., variables that were erroneously captured, missing values).Accurate and comprehensive documentation is particularly critical given that collecting web data tends to involve repeated iterations between discovery (and often troubleshooting) and confirmation (i.e., subsequent analyses that are outside the scope of our framework). Designing web data extractions requires a different mindset compared with experiments or archival research. Unlike running experiments, the extraction design for collecting web data may be in flux, even when the collection is already running. Relative to traditional archival research in which data sets are sufficiently annotated, researchers are in charge of accurately recalling details about the data collection. Such details encompass information about the data composition (e.g., sampled units), extraction process (e.g., annotated code, detected errors during the collection), and processing details (e.g., applied cleaning steps). The template of [24] provides a useful starting point for building the documentation for a data set collected via web scraping or APIs. Given that contextual changes are inevitable (see challenge #1.3), documenting the source's institutional background (e.g., screenshots, corporate blog posts, API documentation) is crucial. Future Research Opportunities with Web DataAn unprecedented gold rush of web data has enriched the marketing discipline for two decades—over 300 published articles provide countless examples of impactful marketing insights using web data. With the ever-increasing digitization of social and commercial life, it is hard to imagine that the heyday of this gold rush might subside any time soon. Yet, are marketing's currently productive mines the only or the most promising sources of marketing insights in the future? Which novel approaches and technologies are necessary to capture and describe evolving marketplace realities?To identify directions for future research, we have reviewed more than 300 articles to provide a snapshot of the current state of web data in marketing. We use these insights to inform the subsequent discussion, which we organize along the four pathways through which web data can advance marketing thought (as summarized in Table 1). We supplement our discussion with key elements from our methodological framework (see Figure 2) and inspiring use cases from other disciplines. Direction 1: Identify New Web Data SourcesNext, we discuss how researchers can use source selection to branch out to new or underutilized sources for studying emerging substantive topics. We also highlight how researchers can design more complex, longitudinal, and multisource web data sets to reveal otherwise invisible phenomena. Draw from underutilized sourcesOur review reveals that marketing research draws from a somewhat concentrated list of web sources (see Table W4 in Web Appendix C). We encourage researchers to focus on underused or niche sources that have received limited or no attention in marketing. Web data are often prized, as they allow for collecting ""consequential dependent variables from the 'real world'"" ([40], p. 357). Identifying new sources or novel consequential variables constitutes a promising avenue for discovering emerging phenomena.Consider, for example, the twilight state of the nascent legal cannabis industry in the United States. While more states are legalizing cannabis for medical and recreational use, the market value of the legal U.S. cannabis industry was still less than a third of the illegal market in 2020 (i.e., $20 billion vs. $66 billion; [22]). Using surveys, media coverage, and in-depth interviews, marketing scholars have begun to explore how such legalized markets emerge and seek legitimacy ([38]). Sociologists and organizational scholars, in turn, have already used web data to compile intriguing data sets from sources such as Weedmaps. Using these data, they examine, for example, how existing medical cannabis dispensaries have repositioned themselves after the entry of recreational dispensaries ([34]) or how consumers deal with potential stigma transfer (Khessina, Reis, and Cameron Verhaal 2021). By leveraging similar web data, marketing researchers could explore intriguing marketing questions. For instance, how should brands position themselves (e.g., brand personalities, emphasis on product vs. service), depending on the strength of categorical stigma? What are the potential public health and welfare implications of the increasing competition among cannabis dispensaries or their growing social media activities?In addition to being attuned to work in other disciplines, a low-tech route for source exploration is provided by Similarweb, which allows researchers to browse website rankings by region or category. Given the broad accessibility of web sources worldwide, the dominance of Northern American and European data sources is surprising. Not a single article focuses exclusively on African web sources, and only a handful of articles use some African data (e.g., [49]). Possible starting points for branching out into these underexplored marketplaces could be popular websites such as Nairaland.com (online community), bidorbuy.co.za (auction platform), and Jumia.com.ng (e-commerce). Build unique and rich data sets by drawing from multiple sourcesMost published marketing articles use web data gathered from a single source. Only very few articles collect data from a large number of web sources (i.e., 50 or more web sources). Following the lead of these articles, we encourage marketing researchers to envision unique data sets compiled from many and diverse sources. For example, in economics, [10] collected online and offline prices for individual goods sold by 56 large multichannel retailers in ten countries (i.e., United States, United Kingdom, Argentina, Australia, Brazil, Canada, China, Germany, Japan, and South Africa) between 2014 and 2016. This ""Billion Prices Project"" (bpp.mit.edu; [11]) exemplifies how creative and ambitious data collection from diverse web sources can fuel entire research programs. Especially if sufficiently documented, such web data are poised to unearth new fields of gold for the marketing discipline. Rediscover frequently used sourcesAs researchers decide which information to extract (see challenge #2.1), they may overlook novel information on sources they already know. Therefore, refocusing on different information may also reveal how to study novel phenomena on frequently used sources. Adopting a ""discovery mode"" may reveal that phenomena of high societal relevance such as gender or racial issues are occurring at frequently used sources such as TripAdvisor ([69]), Kickstarter ([92]), and DonorsChoose ([ 2]). For example, in entrepreneurship, [92] scraped Kickstarter information to examine whether male African American founders are less successful in crowdfunding. Researchers in marketing, in turn, could build on these and similar ideas to explore whether biases exist in other online market exchanges. Alter the extraction frequencyAnother promising lever for exploring emerging phenomena is the extraction frequency (challenge #2.3). In most articles, the data were extracted once (e.g., on a single occasion). Extracting data once is sufficient for many research objectives, such as demonstrating the prevalence of a phenomenon in the marketplace (e.g., [79]). Yet, researchers can also uncover novel marketing phenomena by creatively envisioning web data sets that only reveal the phenomenon if the information is extracted multiple times. For example, [29] leverage the observation that Amazon removed certain reviews to study the market for ""fake"" reviews. Specifically, they combine repeatedly web-scraped data from Amazon with hand-coded data from large private groups on Facebook used to solicit fake reviews to examine the short- and long-term impact of such rating manipulations. This example illustrates that data imperfections (e.g., data modifications discovered when mapping the data context, see challenge #1.3) can be opportunities to pose novel research questions rather than merely nuisances that warrant correction. Direction 2: Harvest the Versatility of Web Data to Boost Ecological ValueAs a second direction for knowledge discovery, web data are often used to increase the ecological value of marketing research by complementing carefully controlled experiments. Triangulating findings generated via different methods is fruitful. Yet, there are many other underutilized avenues for how researchers can select and extract web data to infuse ecological validity into experiments and other types of marketing studies. Infuse ecological validity into experimental stimuliBy carefully selecting websites and APIs, researchers can enhance the ecological validity of their experiments (e.g., through more realistic or diverse stimuli and measures). This enormous potential has hardly been realized in marketing, particularly at scale (for a creative smaller-scale application, see [62]). Social psychologists demonstrate the full potential of such an approach. Consider, for example, [33], who scraped 87 real-world profiles of doctors (including their fitness habits) from the website of a health insurance provider. These profiles served as the foundation for a novel stimulus-sampling paradigm wherein participants in experiments were presented with randomly selected subsets (i.e., five fitness-focused and five non-fitness-focused profiles). In doing so, the authors first ground the phenomenon in the field (i.e., that doctors signal their fitness habits) and then use stimuli created from real profiles to demonstrate that overweight and obese individuals are less likely to choose fitness-focused doctors for their own care. Such triangulation and the creation of larger and more representative samples of naturalistic stimuli enhance the replicability and generalizability of experimental effects ([42]). The experimental paradigms in core marketing topics (e.g., branding, advertising, pricing) and methods (e.g., lab experiments, conjoint studies) could benefit from similar applications to mimic real marketplaces. For instance, branding or advertising researchers might develop stimuli based on data extracted from sources like crowdfunding platforms or Bing's Image Search API (e.g., brand logos, ads, and slogans). Run self-administered field experiments via APIsWhile field experiments continue to be prized for their realism and high ecological value ([83]), very few published marketing articles use APIs to run field experiments (e.g., [51]; [80]). There are many untapped opportunities to run field experiments administered by researchers rather than cooperating partners (e.g., firms or charities). Using APIs to run field experiments gives researchers more control over the design and debriefing processes and allows for monitoring of granular participant behavior over longer periods. Thus, web data–based field experiments potentially produce more precise effect sizes and allow researchers to capture long-term effects ([26]). In such experiments, researchers might randomly assign users to different treatments, such as adding (vs. not adding) followers on Twitter ([80]) or assigning (vs. not assigning) Reddit's Gold Awards to user posts ([ 9]). By gathering high-frequency data via APIs, researchers can analyze how experimental treatments influence outcomes such as posting or the creativity of user-generated content. Alternatively, APIs can be leveraged to infuse realism into experiments, as embodied in [56], who developed ""Hoogle,"" a mock search engine that relies on APIs offered by Google but only displays organic search results that are not altered based on previous user queries. We foresee many more creative future applications of web data to facilitate such field experimentation. Direction 3: Adopt New Metrics and Methods for Generating Marketing InsightsA core topic in marketing research is to develop marketing metrics that can guide managerial decision making. Traditionally, many metrics have been based on offline information and established data providers (e.g., [21]). Given the continued growth and diversification of web data, it is tempting for marketing managers to focus more on web data for managing firm growth and profitability. Yet, deciding which information to select and extract for marketing insight is challenging (see challenge #2.1). More research is needed to help managers avoid succumbing to the streetlight effect (i.e., an ""overreliance on readily available data due to ease of measurement and application, irrespective of their growth objective""; [18], pp. 164–65). But, how can researchers get started? Explore which web sources provide cheaper, faster, or better marketing metricsOver the last decade, scholars have begun to explore which types of web data could proxy or improve on existing core marketing metrics. For example, managers may use search data extracted from Google to spot trends in the relative importance of their firm's product attributes, which is more cost effective than traditional methods ([17]). Mining Twitter data provides cheaper, real-time, and more actionable measures and insights about brand reputation than existing survey-based metrics like the Brand Asset Valuator data from the advertising agency VMLY&R ([71]). Yet, in other circumstances, readily and cheaply available web data might not be a good substitute for more expensive or established proprietary data sources to uncover market structure ([70]).An exciting direction for future research is to explore what web data sources should be selected or combined to generate marketing insights that fuel firm growth. For instance, many novel metrics rely on textual data ([ 6]). This focus limits applications to markets using the same language employed by the original method (i.e., mostly English). Future research could explore what other types of web data might enable the creation of metrics and insights that allow real-time monitoring and managing diverse global markets. What insights can managers draw from differences and commonalities between the volume of different kinds of internet searches available via Google Trends (e.g., web search vs. image search vs. Google Shopping vs. YouTube search)? Alternatively, what insights about consumer preferences (or any other stakeholder) can be extracted from short videos posted on platforms such as TikTok? Operate API-based microservicesA fascinating opportunity arises from providing microservices via APIs to marketing stakeholders. This means that researchers not only use APIs to retrieve data but can also operate their own APIs to examine real marketplaces (e.g., using rplumber.io in R). Researchers in data science, for example, offer firms a framework for testing multiarmed bandit policies via APIs while at the same time gathering field experimental data ([48]). Marketing researchers could use similar API-powered microservices to study emerging topics such as recommendation systems (and resulting biases) or tap into a firm's customer relationship management system to validate new customer churn models. At a small scale, researcher-powered APIs could lower the entry barriers for firms to experiment with novel algorithms that have not yet been implemented in major software packages.The provision of APIs provides access to novel types of data, while also increasing the timeliness and ecological value of such data. For example, consider the differences between web data collected by a web scraper and the underlying clickstream data stored in the company's database. The website may merely show aggregate statistics about the number of reviews posted. At the same time, the underlying clickstream data also feature information on every website visit (e.g., time, IP address). As with self-administered APIs, researchers define which information a company should submit (e.g., as input to a recommendation algorithm). Thus, researchers can gain access to unique firm data that are otherwise difficult to obtain. For example, large-scale studies with image and video data are still scarce in marketing. Offering image and video analysis as microservices may generate knowledge discovery for new image sources, such as GIFs used in social media (e.g., Giphy). Direction 4: Exploit Efficiency Gains to Improve MeasurementWeb data also have advanced marketing by improving measurement by efficiently collecting diverse variables. Therefore, as a fourth direction, we discuss how web data can improve measurement across the discipline, particularly by rejuvenating interest in core marketing topics (e.g., market orientation, advertising; for an overview of these topics, see [41]). Relatedly, researchers can also leverage APIs to effectively integrate algorithms for processing unstructured data at scale into empirical analyses ([88]). Leverage web sources to describe diverse online and offline behaviorsMost marketing articles gather web data to describe and examine behavior occurring online. As documented in Table W4 in Web Appendix C, many of the used sources in marketing are focused on online consumer behaviors, such as e-commerce websites (e.g., Amazon), online reviewing platforms (e.g., Yelp), social media sites (e.g., Twitter), and search engines (e.g., Google Trends). Relatively less research has focused on firm behavior online. Yet, by doing so, researchers could explore many core marketing constructs (e.g., service orientation, sustainability). For example, researchers could systematically collect information available on the websites of many firms to analyze which organizational factors influence how firms signal their service orientation (e.g., employees' digital presence; [30]) or environmental credentials (e.g., the B Corporations certification; [25]) to customers and other stakeholders.We encourage marketing researchers who have not yet used web data in their research to consider websites and APIs as valuable, rich, and timely sources to exploit the increased digitization of all forms of behaviors—not only online behavior. A recent example of bringing web data into an established ""offline"" research stream is [32], who scraped the annual reports of more than 8,000 firms from AnnualReports.com between 1998 and 2016. Web sources contain historical information about periods, even long before the web in its current form existed (e.g., 1998 in this case). The authors subsequently use these reports to develop a novel text-based measure of marketing excellence derived from firm letters to shareholders. Many other untapped online sources (e.g., job posting platforms) offer new insights into how firms communicate their marketing capabilities to external stakeholders beyond consumers, such as prospective employees, social activists, and investors.Particularly for the marketing–finance interface, the web features many understudied forms of investor-facing communication that are ripe for collection at scale. For example, which type of marketing topics besides marketing excellence (e.g., marketing capabilities, brand positioning, pricing) should top management emphasize to investors to increase firm valuation during investor relations presentations, investor days, or earnings calls? Researchers could also examine the relative importance of the content versus the delivery (e.g., the tone of the speaker on the recording of an investor day; see [85]). Such multimodal data can also benefit the inferences made in established research streams. Embrace APIs for better measurementAPIs offer many opportunities for improving measurement—some of which are unexpected. For example, consumer researchers planning to run longitudinal studies might consider APIs for automating processes for managing participants at scale, thereby reducing the operating costs (and potentially boosting sample size). The Amazon Mechanical Turk API and the various Prolific Academic APIs (e.g., Study API) are good starting points for running multiwave studies.APIs also enable much more than just retrieving data. For example, to reduce validity concerns in long-term data collection, researchers can use the Pushover API (https://pushover.net/api) to send monitoring alerts to their smartphones. The API of Amazon Web Services allows for the orchestration of virtual computing infrastructure (e.g., to capture data from different countries). Another fruitful avenue in which APIs are currently underused in marketing is facilitating stimuli selection. For example, a classic area of inquiry in marketing is how (background) music affects product and brand perceptions and choices (e.g., [ 8]). In 2022, background music is quite different (e.g., self-chosen, more diverse use cases). Researchers could use the Spotify Web API to select stimuli from millions of mood, sleep, or study playlists, thereby discovering perfect ""lookalikes"" that only differ on one focal attribute (e.g., tempo) but not on other acoustic attributes available at the API (e.g., valence, loudness). Even in this simple example, there might be a substantive interest in better understanding the effect of new background music on consumption choices, especially given the shift to working and studying from home. Concluding ThoughtsWeb data have unearthed many fields of gold in marketing. However, extracting data for generating relevant and valid research insights is challenging. Our article highlights validity concerns that require the joint consideration of idiosyncratic technical, legal, and ethical questions. We introduce a novel methodological framework (Figure 2), offer practical solutions (Tables 2–4), and outline directions for future research to enable researchers to create impactful and credible marketing knowledge. While our focus is primarily on authors, our work also spotlights crucial validity concerns to scholars reviewing web data–based research and practitioners interested in deriving accurate and actionable marketing insights from web data.We hope that our work encourages marketing scholars to integrate web data into their research programs. While web data often provide compelling answers to the question, ""Assuming that this hypothesis is true, in what ways does it manifest in the world?"" ([ 5], p. 1455), this does not imply that web data are relevant for all research projects. Web data sources tend to feature a large N (i.e., many users) with many V (i.e., different pieces of information for potential variables) observable over a large T (i.e., many observations over extended periods of time and at a very granular level; [ 1]). Yet, collecting web data via web scraping or APIs provides limited information about the browsing behavior of individuals on the website that led to the creation of the data in the first place. Significant synergies exist by enriching clickstream stream data capturing such browsing processes with web data retrieved from web scraping and APIs (e.g., [54]).Our work aims to bridge entrenched training silos (e.g., between quantitative marketing and consumer behavior). We encourage scholars to further integrate and leverage existing best practices with regard to the collection and analysis of web data (e.g., preregistration, addressing endogeneity). There is significant untapped potential for collaborations across methodological traditions to explore and exploit new fields of gold. Collecting valid web data can enable marketing as a discipline to enhance its relevance and assert intellectual leadership on important emerging substantive topics that are also increasingly studied in fields such as computer science, information systems, and management science ([63]).We would be remiss not to mention the nonmonetary costs of collecting web data via web scraping and APIs. While browsing the web is (mainly) free, researchers should not assume that collecting web data is costless. The prototype of a data collection can be ready and running in a matter of hours. Yet, researchers will often find out that the data collection does not work entirely as intended or encounter some of the challenges discussed in our methodological framework. Just like with any other method, the devil is in the details.Web data democratize data access and make our discipline more inclusive for scholars who would otherwise find it difficult to obtain access to data. To further reduce entry barriers, it would be helpful to create incentives (e.g., journal space) for rich web data sets and their documentation, like the Billion Prices Project ([11]). Similarly, authors can make their algorithms or data available for other researchers by sharing code publicly or deploying API-based microservices that can increase their methods' adoption and offer unique opportunities for field experimentation. In summary, web data present a golden opportunity to examine important marketing questions, now and in the future. " 23,"From Waste to Taste: How ""Ugly"" Labels Can Increase Purchase of Unattractive Produce"," Food producers and retailers throw away large amounts of perfectly edible produce that fails to meet appearance standards, contributing to the environmental issue of food waste. The authors examine why consumers discard aesthetically unattractive produce, and they test a low-cost, easy-to-implement solution: emphasizing the produce's aesthetic flaw through ""ugly"" labeling (e.g., labeling cucumbers with cosmetic defects ""Ugly Cucumbers"" on store displays or advertising). Seven experiments, including two conducted in the field, demonstrate that ""ugly"" labeling corrects for consumers' biased expectations regarding key attributes of unattractive produce—particularly tastiness—and thus increases purchase likelihood. ""Ugly"" labeling is most effective when associated with moderate (rather than steep) price discounts. Against managers' intuition, it is also more effective than alternative labeling that does not exclusively point out the aesthetic flaw, such as ""imperfect"" labeling. This research provides clear managerial recommendations on the labeling and the pricing of unattractive produce while addressing the issue of food waste.","Consumers today expect the fruits and vegetables they purchase to ""look good"" all year round ([56]), a demand that farmers and retailers meet by discarding large amounts of produce that fails to meet aesthetic standards. A lot of produce fails these standards, not because of disease or damage that may negatively affect taste or nutritional quality, but simply because of inherent variation in natural growth. In fact, U.S. retailers throw away $15.4 billion of edible produce each year ([ 7]), and farmers discard up to 30% of their crops because of cosmetic imperfections ([ 5]). Food waste also has damaging consequences for the environment: 96% of wasted food is left to decompose in landfills, resulting in the release of methane, a greenhouse gas that traps solar radiation and contributes to climate change ([14]). In addition, food waste leads to a waste of other valuable resources: 1.4 billion hectares of land and 25% of the world's fresh water are used to grow produce that will be later thrown away ([22]; [40]).Recent research has started to identify factors that might increase consumers' acceptance of unattractive produce, including marketing message framing ([16]; [48]; [53]), reduced pricing ([ 2]), and individual differences in environmental awareness ([11]; [36]; [57]). Most relevant to the present investigation, [16] proposed that consumers devalue unattractive produce, in part because imagining eating it negatively affects how they view themselves. Thus, a marketing message boosting consumers' self-esteem, ""You Are Fantastic! Pick Ugly Produce!,"" increases purchase compared with a message simply stating, ""Pick Ugly Produce."" While this research identifies a straightforward managerial intervention, both the intervention and the comparison messages labeled unattractive produce ""ugly,"" so the effect of ""ugly"" labeling in isolation is unclear.We build on this prior work by investigating the effect of labeling unattractive produce as ""ugly"" (what we call ""ugly"" labeling) and comparing it with several alternative labels. We believe that further investigation is warranted because most retailers do not label unattractive produce in any specific way, and when they do, there is great variation in how unattractive produce is labeled. Indeed, although ""ugly"" labeling was employed by French retailer Intermarché in 2014, subsequent campaigns by other retailers have used more understated labels to promote unattractive produce, such as ""imperfect"" or labels that aim to positively frame visual atypicality, such as produce ""with personality."" To assess managers' beliefs regarding the use of ""ugly"" labeling, we interviewed 52 grocery store managers across North America with an average of 12 years of experience, and asked them to indicate which of four labeling options (""ugly,"" ""imperfect,"" ""with personality,"" or no specific label) they would use to promote unattractive produce sold at a discounted price. Of the 52 respondents, 46% stated that they would not use any label and that just the discount was enough, followed by 33% preferring ""imperfect"" labeling, 17% preferring ""with personality"" labeling, and only 4% preferring ""ugly"" labeling. We also asked them to select the worst option, and 75% mentioned ""ugly"" labeling.Although managers do not see merit in ""ugly"" labeling, our research proposes that labeling unattractive produce as ""ugly"" can increase purchase, not only compared with no specific labeling, but also compared with more understated—and more popular—labeling such as ""imperfect."" We demonstrate the effectiveness of ""ugly"" labeling through a combination of field and online experiments, and elucidate the underlying mechanism. We show that consumers saddle unattractive produce with an ""ugliness penalty"" ([23], p.1181) that negatively affects expectations of the produce's key attributes—particularly tastiness—and thus affects purchase intentions. ""Ugly"" labeling corrects for these biased, negative expectations because it directly points out the aesthetic flaw as their source, in line with research that has shown a corrective effect when drawing observers' attention toward the source of a biased judgment ([49]; [51]). Further, while price discounts can motivate consumers to purchase unattractive produce ([ 2]), we show that ""ugly"" labeling is most effective when associated with a moderate price discount, because large discounts in conjunction with the ""ugly"" label send conflicting signals regarding the quality of the produce.Our work makes several contributions. While prior research on two-sided persuasion ([12]; [42]) has shown that weak negative information added to a positive description can improve product evaluation, our research demonstrates that emphasizing negative information can have positive effects in the absence of any accompanying positive information. We also contribute to research that has investigated how awareness of influence has a corrective effect on biased judgment ([49]), extending prior findings to a consumption context.Our research also provides guidance to managers on how to label and price unattractive produce. While retailers believe ""imperfect"" labeling or no specific labeling to be more effective than ""ugly"" labeling, we demonstrate that the opposite is the case. Our research may therefore partly explain the unsuccessful attempts by Whole Foods and Walmart to sell unattractive produce by labeling it ""imperfect"" ([ 9]). Our hope is that our research can assist managers in designing campaigns that can benefit their organizations and reduce food waste. Theoretical Background Why Consumers Reject Unattractive Produce: The ""Ugliness Penalty"" EffectUnattractive produce is that which has a significant natural aesthetic deviation in shape and/or color from prototypical produce, but has no damage or disease that could affect safety, taste, or nutrition ([11]; [16]). [16] suggest that consumers reject unattractive produce because imagining eating such produce makes consumers view themselves as less attractive, less moral, less healthy, and so on. We propose that produce unattractiveness also influences how consumers view the produce itself. Extant research in social and consumer psychology shows that people stereotypically attribute a ""beauty premium"" to attractive individuals and objects, and, conversely, they saddle unattractive individuals and objects with an ""ugliness penalty"" that negatively affects perceptions beyond aesthetics. Indeed, physically unattractive individuals are perceived as less intelligent and less sociable than attractive individuals ([17]), and unattractive products are perceived as lower in quality and usability ([26]; [55]).Regarding potential ""ugliness penalty"" effects in the realm of produce, we consider three categories of attributes: tastiness, healthiness, and naturalness. Tastiness refers to produce's hedonic, multisensory qualities: not only its flavor, but also its juiciness or crispiness ([ 3]). Healthiness refers to nutritional value. Naturalness refers to the absence of chemicals (e.g., pesticides, preservatives), which is characteristic of organic produce ([54]). In addition to these categories, there can be additional safety concerns in the case of moldy, rotten, or damaged produce. However, our definition of unattractive produce explicitly excludes these concerns as retailers have strict regulations preventing the sale of unsafe produce. TastinessThere is clear evidence in the literature of a positive association between aesthetic appeal and tastiness—thus, consumers should expect unattractive produce to be less tasty than attractive produce. Visual appearance, including color and shape, has a strong impact on inferences about a food's sensory quality ([11]; [29]). In the domain of produce, multiple studies have shown that a wide range of fruits and vegetables with atypical (vs. typical) colors were expected to be less tasty, although the actual taste was equivalent ([34]; [47]; [50]). HealthinessResearch also points to a positive association between aesthetic appeal and healthiness. In the domain of produce (as opposed to many processed foods), consumers largely expect tasty foods to be healthier ([19]), so if unattractiveness negatively affects tastiness expectations, it should also negatively affect healthiness expectations. In line with this proposition, two studies have found that carrots with atypical (vs. typical) colors and bell peppers with uneven (vs. even) shape were expected to be not only less tasty, but also less healthy ([20]; [47]). NaturalnessThe association between attractiveness and naturalness is less straightforward. On the one hand, classic aesthetic patterns that are considered beautiful (e.g., the golden ratio, Fibonacci proportions) stem from the natural world ([41]; [45]), hinting at a possible positive correlation between perceived attractiveness and naturalness—as [20] found in the domain of food presentation. On the other hand, within the domain of fresh produce, cosmetic imperfections generally stem from nature ([16]); thus, there may rather be a negative correlation between attractiveness and naturalness expectations. In line with this perspective, several studies suggest that consumers expect natural, organic, and/or pesticide-free produce to be less attractive ([ 6]; [15]; [52]; [58]), especially eco-conscious consumers ([35]). Importantly, the expectation that unattractive produce is more natural is not biased, but due to the fact that the absence of chemicals (pesticides, preservatives) results in cosmetic imperfections ([ 6]). The Corrective Effect of ""Ugly"" LabelingAs detailed previously, prior literature suggests that consumers expect unattractive produce to be less tasty and less healthy. Expectations regarding naturalness are less clear but tend toward a reverse effect given that natural/organic produce is more likely to be visually imperfect. Note that there is no factual reason to expect unattractive produce to be less tasty or less healthy; in fact, assuming that unattractive produce is more natural/organic, it should also be more tasty and more healthy, as suggested by a meta-analysis of 343 publications that concluded that organic foods present both gustatory and nutritive benefits ([ 4]). Thus, negative expectations regarding the tastiness and healthiness of unattractive produce are biased judgments, based on stereotypes such as those uncovered in research on the ""ugliness penalty.""We posit that ""ugly"" labeling—that is, labeling unattractive produce ""ugly""—will correct for negative, biased expectations that consumers may have about the tastiness or healthiness of unattractive produce. We propose that deliberately emphasizing the unattractiveness of the produce via ""ugly"" labeling acts as a signal that there is nothing ""wrong"" with the produce other than its appearance. Further, ""ugly"" labeling may make consumers reevaluate the diagnosticity of visual appearance for assessing tastiness and healthiness; that is, it will make them aware of the limited nature of their spontaneous objection to unattractive produce. This proposition is in line with research that has shown that ""awareness of influence"" triggers validity-driven corrections of attitudes ([49]). For instance, in the domain of aesthetics, [51] found that the aesthetic design of financial documents influenced participants' investment decisions, unless their attention was drawn to the design.In summary, our central hypotheses are that ""ugly"" labeling will increase purchase of unattractive produce versus when no specific label is present and that this will occur by improving attribute expectations, in particular tastiness and healthiness. We do not expect naturalness expectations to be affected by ""ugly"" labeling, insofar as consumer beliefs about unattractive produce being more natural are not instances of biased judgment and as such do not need correction. Formally, H1: ""Ugly"" labeling (vs. no specific label) increases the likelihood that consumers purchase unattractive produce. H2: The effect of ""ugly"" labeling on the purchase of unattractive produce is mediated by improved attribute expectations, particularly tastiness and healthiness. ""Ugly"" Labels Versus Common Marketplace InterventionsIt is important to consider how ""ugly"" labeling compares or interacts with other interventions investigated by past research and/or employed in the field. First, research has shown that price discounts can motivate consumers to purchase unattractive produce ([ 2]; [11]); indeed, it is common practice to sell unattractive produce at a discount of up to 50% ([16]). However, we propose that the depth of the discount moderates the effectiveness of ""ugly"" labeling. Although consumers value the economic benefit of acquiring produce for a low price, a large discount may signal low quality ([18]), thereby hindering the corrective effect of ""ugly"" labeling. From a managerial perspective, this suggests that ""ugly"" labeling along with a moderate discount may be as effective as a steeper discount in motivating purchase. H3: The effect of ""ugly"" labeling on purchase is moderated by the depth of price discount, such that ""ugly"" labeling is most effective when associated with a moderate (vs. steep) discount.Second, while ""ugly"" labeling has generated a lot of media attention, there is great variation in the marketplace on the labeling of unattractive produce. In fact, major brick-and-mortar retailers such as Whole Foods, Loblaws (in Canada), and Tesco (in England), as well as online retailers Imperfect Foods (imperfectfoods.com) and Perfectly Imperfect Produce (perfectlyimperfectproduce.com), have preferred to use a more understated label: ""imperfect."" Retailers have also utilized labels that attempt to positively frame visual atypicality, such as ""produce with personality"" (Giant Eagle), ""misfit"" (Hy-Vee), or ""pickuliar"" (Koger). Web Appendix W1 provides a nonexhaustive list of labels used by retailers all over the world.We have argued that ""ugly"" labeling unambiguously points out the aesthetic flaw in the produce, making it clear that there are no deficiencies other than unattractiveness. For this reason, alternative labels that do not point to the aesthetic flaw should not improve attribute expectations as much as ""ugly"" labeling does, and should therefore be less effective at motivating purchase. We compare ""ugly"" labeling with ""imperfect"" labeling (because it is the most popular label and does not point directly to aesthetics) and ""with personality"" labeling (as an example of a label that positively frames visual atypicality). H4: ""Ugly"" labeling is more effective than alternative labeling that does not explicitly point out the aesthetic flaw. Overview of StudiesWe first test the effectiveness of ""ugly"" labeling in the field at a farmers' market (Study 1) and online, with incentive-compatible choices (Study 2). We then test our proposed mechanism—an increase in tastiness and healthiness expectations—through mediation (Study 3) and moderation (Study 4). We further test whether the effectiveness of ""ugly"" labeling is moderated by price discounts (Study 5). Finally, we compare the effectiveness of ""ugly,"" ""imperfect,"" and ""with personality"" labeling in an online study (Study 6a) and in a field study measuring online advertising click-throughs (Study 6b). Study 1: Field Experiment at a Farmers' MarketIn Study 1, we tested the effect of ""ugly"" labeling at a farmers' market. We ran a stand selling attractive and unattractive vegetables, and manipulated the way the unattractive produce was labeled (either ""ugly"" or not) by changing signage every hour. This study was preregistered (http://aspredicted.org/blind.php?x=zg7hi5). PretestWe obtained visually attractive and unattractive carrots, potatoes, and tomatoes from a local supplier. The unattractive vegetables were crooked or oddly shaped, but were not bruised or rotten. Fifty participants recruited on Amazon Mechanical Turk (MTurk)[ 5] rated photos of these vegetables from −3 = ""Much less beautiful than normal"" to +3 = ""Much more beautiful than normal,"" with a midpoint of 0 = ""Normal-looking."" Participants judged the unattractive vegetables as less beautiful than the attractive vegetables (carrots: M = −1.20, SD = 1.46 vs. M =.34, SD = 1.17; p <.001; tomatoes: M = −.66, SD = 2.05 vs. M = 1.34, SD = 1.21; p <.001; potatoes: M = −.36, SD =.94 vs. M =.86, SD = 1.31; p <.001). MethodWe conducted the study at a farmers' market in a major city in Canada over four consecutive Saturdays in September 2020. We ran a stall from 10:00 a.m. to 2:00 p.m. each day, for a total of 16 hours. The stall consisted of a tent and a table, to which was attached a poster stating the name of the stand (""Sam's Produce"") and, per a request by the Association of Farmers' Markets, indicating that the stand is a student project selling certified organic produce grown by local farmers (see Web Appendix W2).On top of the table were four baskets (see Figure 1): two contained unattractive produce, and two contained attractive versions of the same produce. We used potatoes and carrots on the first day, and potatoes and tomatoes on the other three days because carrots were no longer available from our supplier. The baskets had labels attached to them. We manipulated the labels associated with the unattractive produce, such that it was explicitly called ""ugly"" in the ""ugly"" label condition (""Ugly Potatoes,"" ""Ugly Carrots,"" ""Ugly Tomatoes"") and not in the control condition (""Potatoes,"" ""Carrots,"" ""Tomatoes""). Across both conditions, the attractive produce was always labeled ""Potatoes,"" ""Carrots,"" and ""Tomatoes."" We changed the labels used for the unattractive produce every hour. On the first and third days, we displayed the ""ugly"" label first (from 10:00 to 11:00 a.m.), while on the second and fourth days, we displayed the control label first.Graph: Figure 1. Stimuli.Notes: For Studies 2–4, we only show stimuli in the ""ugly"" label condition. Stimuli in the control condition were identical, but without the label ""ugly."" All stimuli can be found in Web Appendix W5.Our pricing was consistent across conditions. Following prior research ([16]) and within the range of industry practice, the unattractive produce was sold at a discount of 25%. The attractive potatoes, carrots, and tomatoes were respectively priced at CAD $2.50, $2.50, and $3.00 per pound, while the unattractive potatoes, carrots, and tomatoes were respectively priced at CAD $1.88, $1.88, and $2.25 per pound.The stall was managed by two research assistants blind to the hypotheses. The first research assistant was in charge of switching the labels and acted as the seller, handling transactions and communicating with shoppers following a script prepared in advance and kept constant across conditions. To maximize control, the research assistant was instructed to evade the issue if shoppers asked about the labels. A second research assistant recorded the transactions and also recorded the number of individuals per hour who stopped at the stand and engaged with the seller. ResultsAcross the four days, 938 individuals (in 573 groups) stopped at the stand, and 259 individuals (in 169 groups) engaged with the seller. Two-sided binomial tests indicated no significant differences in the number of individuals stopping or engaging with the seller across labeling conditions (all ps >.21). There were 113 buyers (defined as the individuals who handled money to purchase produce), but again, there was no significant difference in the number of buyers across labeling conditions (p =.38), although labeling affected what produce was bought, as shown in the following analyses.It was unknown whether the buyers purchased produce for themselves or also for the individuals that accompanied them, or whether the buyers used their own money or the group's pooled money. Therefore, as indicated in the preregistration, all analyses controlled for the size of the group (if a buyer was alone, group size was 1; mean group size was 1.56). The analyses also controlled for the day of the study, given that we replaced carrots with tomatoes after the first day. All effects remained significant without these covariates (see Web Appendix W3).In the control condition, 62.5% of buyers purchased unattractive produce and 56% purchased attractive produce (these proportions do not total 100% because some buyers purchased both types of produce). In the ""ugly"" label condition, 81.6% bought unattractive produce and 26.5% bought attractive produce. Two logistic regressions showed that labeling unattractive produce ""ugly"" (vs. control) significantly increased buyers' likelihood to purchase unattractive produce (z = 2.28, p =.02) and decreased their likelihood to purchase attractive produce (z = −3.06, p =.002).We found converging results using spending as the dependent variable (see Figure 2). On average, in the control condition, buyers purchased $2.36 (SD = 2.49) of unattractive produce and $3.35 (SD = 4.34) of attractive produce. In the ""ugly"" label condition they purchased $3.41 (SD = 2.83) of unattractive produce and $1.78 (SD = 3.76) of attractive produce. A mixed regression of total spending, with label (""ugly"" vs. control) as a between-subjects factor and appearance (attractive vs. unattractive) as a within-subject factor, found no significant main effects (all ps >.49) but a significant interaction effect (z = 2.83, p =.005). To interpret this interaction effect, we ran a multivariate regression with spending on unattractive produce and spending on attractive produce as dependent variables. Labeling unattractive produce ""ugly"" (vs. control) significantly increased spending on unattractive produce (t(107) = 2.16, p =.03) and marginally decreased spending on attractive produce (t(107) = −1.79, p =.08).Graph: Figure 2. Spending by label conditions and visual appearance of produce (Study 1).†p <.1.*p <.05Notes: Error bars: ±1 SE. DiscussionEmploying a field study setting at a farmers' market, we found that buyers were more likely to purchase unattractive produce (sold at a discounted price) over attractive produce when the unattractive produce was labeled ""ugly,"" compared with a control condition in which unattractive produce was not labeled in any specific way. ""Ugly"" labeling also increased average spending on unattractive produce. These results verify H1 and go against managers' intuition that merely discounting unattractive produce, without using any specific label, should be more effective than using an ""ugly"" label.In absolute terms, since the ""ugly"" label increased purchase of cheaper (unattractive) over more expensive (attractive) produce, less total revenue was generated in the ""ugly"" label condition ($254.50) than in the control condition ($364.90). However, given that attractive produce is more costly (not to mention its environmental cost), after including the cost at which we purchased the produce from the suppliers, gross profit margins were higher in the ""ugly"" label condition ($39.30) than in the control condition ($26.00). Study 2: Incentive Compatibility and Option to Defer PurchaseIn Study 2, we further test the effectiveness of ""ugly"" labeling in the context of produce boxes purchased online. Participants decided whether to buy a box of unattractive produce or a box of attractive produce (or nothing at all), and we manipulated the label for the unattractive produce (either ""ugly"" or not). We used an incentive-compatible design, and this study was preregistered (https://aspredicted.org/blind.php?x=hd3iu5). All questions for this and all subsequent studies appear in Web Appendix W4. PretestOur stimuli consisted of a photo of attractive oranges, apples, cucumbers, and carrots, and a photo of the same items but visually unattractive (see Figure 1). Fifty MTurk participants judged the unattractive produce less beautiful than the attractive produce (M = −1.90, SD = 1.39 vs. M = 1.20, SD = 1.12; p <.001). MethodBecause this study involved incentive-compatible choices and to increase the power of the study ([37]), we only recruited participants who would potentially be interested in purchasing produce online. We posted an ad on Facebook (shown in Web Appendix W5) targeted at people living in the United States, between 18 and 64 years of age, with an interest (determined by the Facebook pages they ""like"") in ""Online grocer,"" ""FreshDirect,"" and ""AmazonFresh."" The ad indicated that our research team was looking for participants, and in exchange for completing a survey, they would enter a lottery to win $30 or produce boxes. The ad never mentioned ""ugly"" produce to avoid recruiting participants with a specific interest in such produce. We advertised the study until 303 participants completed it (Mage = 45.20 years, SD = 12.83 years; 93% female). The high proportion of female participants is likely due to Facebook ad targeting. Participants were randomly assigned to one of two conditions: either ""ugly"" labeling or control.The ad led to a study hosted on Qualtrics. In the consent form, we indicated that the chance of winning the lottery was about 15%. Then, as a cover story, participants answered 25 questions with two possible answers, reportedly designed to measure personality (e.g., ""Would you rather go to a movie or to dinner alone?""). In the 25 questions, we embedded two attention checks that automatically excluded participants who failed, before they could participate in the actual study (see Web Appendix W4).Next, participants read, ""You will now enter a lottery to win $30. The prize will be paid via PayPal, Amazon eGift card or other online means of payment of your choice. If you win, you can decide to keep the $30, or to use some of this money to purchase a box of fruits & veggies delivered to your doorstep by one of our trusted partners. Produce sold by our partners meets USDA [U.S. Department of Agriculture] safety standards. We managed to get special deals on two boxes of fruits & veggies."" We provided illustrations and information about these two boxes. Box 1 featured attractive oranges, apples, carrots, and cucumbers and indicated ""SPECIAL PRICE: $20 (regular price: $35),"" and Box 2 featured the same produce but aesthetically unattractive and indicated ""SPECIAL PRICE: $15 (regular price: $25)."" The label used for the attractive produce was always ""Fruits and Veggies."" We manipulated between subjects the label used for the unattractive produce: either ""Ugly Fruits and Veggies"" in the ""ugly"" label condition or ""Fruits and Veggies"" in the control condition. We show the stimulus used in the ""ugly"" label condition in Figure 1, and all stimuli in Web Appendix W5.Participants were asked to indicate in advance what they would do if they won the lottery: ""I want the full $30 cash prize without buying anything,"" or ""I want Box 1 at a special price of $20 delivery included, and I get the remainder of $10 cash,"" or ""I want Box 2 at a special price of $15 delivery included, and I get the remainder of $15 cash.""We programmed the survey such that 15% of the participants won the lottery. The winners provided their email address, and we followed up by sending them online cash payments and/or online coupons of produce box delivery companies (Farmbox Direct, Farm Fresh to You, Hungry Harvest, and Perfectly Imperfect Produce), depending on what prize they selected. If none of the companies could deliver to their address, we sent them online cash payments. ResultsIn the ""ugly"" label (vs. control) condition, 41.1% of participants (vs. 26.3%) decided to purchase the box of unattractive produce, 7.9% (vs. 23.0%) decided to purchase the box of attractive produce, and 51.0% (vs. 50.7%) preferred to keep the cash (see Web Appendix W6). A logistic regression showed that the likelihood of purchasing a box over keeping the cash was not different across conditions (p =.95). However, the ""ugly"" label (vs. control) significantly increased the likelihood of purchasing the box of unattractive produce over the box of attractive produce (z = 3.86, p <.001). DiscussionIn an online study with an incentive-compatible measurement of choice and where participants had the option not to purchase any produce, we found that ""ugly"" labeling made consumers purchase unattractive, rather than attractive produce, in line with H1. As in Study 1, ""ugly"" labeling influenced produce choice, but not overall produce purchase. Study 3: Mediation by Tastiness and Healthiness ExpectationsIn Study 3, we test our proposed mechanism: we posit that consumers have negative expectations regarding the tastiness and healthiness (but not the naturalness) of unattractive produce, and that ""ugly"" labeling improves these expectations. The study also addresses several alternative explanations for the positive effect of ""ugly"" labeling on choice. For example, it is possible that ""ugly"" labeling is perceived as original, surprising, or amusing ([13]). Likewise, ""ugly"" labeling may anthropomorphize unattractive produce, increasing sympathy ([32]; [48]). ""Ugly"" labeling might also enhance the perceived credibility of the seller by conveying honest information about the produce. Finally, ""ugly"" labeling might affect self-perceptions ([16]). We thus measure each of these constructs to test their potential role. The study was preregistered (http://aspredicted.org/blind.php?x=ah63mh). PretestWe used photos of attractive and unattractive cucumbers. Fifty MTurk participants judged the unattractive cucumbers less beautiful than the attractive ones (M = −.84, SD = 1.54 vs. M = 1.26, SD = 1.24; p <.001). MethodWe assigned 320 MTurk participants (Mage = 36.21 years, SD = 11.94 years; 53% female) to one of two between-subjects conditions: ""ugly"" label versus control. Participants were shown photos of baskets of attractive and unattractive cucumbers ostensibly sold by the same vendor and meeting USDA safety standards. Across conditions the attractive cucumbers were called ""Type A"" and priced at $1.26 per pound, and the unattractive cucumbers were called ""Type B"" and priced at $.95 per pound. We manipulated the label attached to the basket of unattractive cucumbers: ""Ugly Cucumbers"" in the ""ugly"" label condition versus ""Cucumbers"" in the control condition. The attractive cucumbers were always labeled ""Cucumbers."" The stimuli for the ""ugly"" label condition appear in Figure 1, and all stimuli in Web Appendix W5.Participants indicated which produce they would purchase on a five-point scale ranging from 1 = ""Definitely Cucumbers A"" to 5 = ""Definitely Cucumbers B,"" with a midpoint of 3 = ""I would be indifferent.""We then measured produce attribute expectations ([28]) with a scale composed of four taste-related items (tasty, flavorful, juicy, crisp), three health-related items (healthy, nutritional, full of vitamins), four nature-related items (natural, free of pesticides, free of preservatives, organic), and three other items (ripe, fresh, clean). For each item, we asked participants to rate their expectations of Cucumbers B relative to Cucumbers A on a seven-point scale ranging from −3 = ""Much more negative than Cucumbers A"" to 3 = ""Much more positive than Cucumbers A,"" with a midpoint of 0 = ""Not different from Cucumbers A.""The next measurements were used to test alternative explanations. We distributed the negative self-perception scale developed by [16]: participants imagined eating Cucumbers B (i.e., the unattractive ones) and rated whether they felt 16 self-perceptions (e.g., worthless, immoral) on a seven-point scale (1 = ""Not at all,"" and 7 = ""Very much""). Credibility was assessed with four items (e.g., ""I think the seller of this vegetable is trustworthy"") adapted from [31] and evaluated on a seven-point scale (1 = ""Strongly disagree,"" and 7 = ""Strongly agree""). We measured anthropomorphic perceptions by asking participants to rate whether Cucumbers B reminded them of humanlike features ([32]) on a five-point scale (1 = ""Not at all,"" and 5 = ""To a great extent""). We also asked participants whether they ""feel sorry,"" ""feel compassion,"" and ""feel sympathy"" for Cucumbers B on the same five-point scale. We measured whether participants perceived the image of cucumbers B to be original, surprising, and funny (with two items: funny and amusing) on a five-point scale (1 = ""Not at all,"" and 5 = ""To a great extent""). Each construct was presented on a separate, randomized page with reminders of the stimuli.At the end of the study, as an attention check, we asked participants to recall the prices of Cucumbers A and B. There were five possible answers and only one correct answer; those who answered incorrectly were excluded from analysis. We used the same preregistered attention check and exclusion rule across all MTurk studies (Studies 3–6a). In Web Appendix W7, we report results with and without data exclusion; the results are consistent. ResultsTwenty-eight participants (8.8%) failed the attention check and were excluded from analysis. Choice likelihoodAn analysis of variance (ANOVA) of choice likelihood indicated that ""ugly"" labeling (vs. control) increased the likelihood of choosing the unattractive produce over the attractive produce (M = 3.01, SD = 1.44 vs. M = 2.54, SD = 1.42; F( 1,290) = 7.90, p =.005). Attribute expectationsFigure 3 displays expectations about each attribute across conditions. We created indices of tastiness expectations (α =.92), healthiness expectations (α =.92), and naturalness expectations (α =.91) and performed the analyses using these indices. Although ""ripe,"" ""fresh,"" and ""clean"" contribute to taste and nutritive quality, they are conceptually distinct from the tastiness and healthiness constructs ([27]; [43]), so we did not include these items in the indices; note that ""ugly"" labeling did not significantly improve ""ripe,"" ""fresh,"" and ""clean"" expectations (all ps >.10).Graph: Figure 3. Attribute expectations of visually unattractive produce by label conditions (Study 3).*p <.05.**p <.01.Notes: Error bars: ±1 SE. ""Tastiness,"" ""healthiness,"" and ""naturalness"" are indices composed of the items in the brackets.The tastiness index was well below zero in the control condition (p <.001), indicative of an ""ugliness"" penalty effect on taste expectations of unattractive produce. The ""ugly"" label (vs. control) improved the tastiness index (M = −.08, SD = 1.06 vs. M = −.45, SD = 1.12; F( 1, 290) = 8.45, p =.004). Healthiness expectations in the control condition were not significantly different from zero (p =.93). Still, the ""ugly"" label (vs. control) significantly increased the healthiness index (M =.23, SD =.94 vs. M = −.01, SD =.97; F( 1, 290) = 4.59, p =.03), although to a smaller extent than the tastiness index. We found an ""ugliness premium"" for naturalness, with expectations above zero in the control condition (p =.01), and the ""ugly"" label did not further increase the naturalness index (M =.38, SD = 1.03 vs. M =.26, SD = 1.20; p =.34).We conducted a mediation analysis ([25], Model 4) with the tastiness, healthiness, and naturalness indices as parallel mediators, choice likelihood as the dependent variable, and the label manipulation as the independent variable. As shown in Figure 4, tastiness had the strongest mediating effect (b =.17, SE =.07, 95% confidence interval [CI] = [.055,.343]), healthiness had a weaker, although significant, mediating effect (b =.08, SE =.05, 95% CI = [.008,.226]), and naturalness did not have a mediating effect (95% CI = [−.029,.137]). We conducted the same analyses for comparable conditions in Studies 4, 5, and 6a and present them in Figure 4.Graph: Figure 4. Mediation by tastiness, healthiness, and naturalness expectations (Studies 3 to 6a).*p <.05.**p <.01.***p <.001.Notes: Parallel mediation models ([25], Model 4) were used for Studies 3, 4, 5, and 6a. We only consider comparable ""ugly"" label and control label conditions: for Study 4, we exclude the condition in which participants received the corrective message; for Study 5, we exclude the two larger discount (40% and 60%) conditions; for Study 6a, we exclude the ""imperfect"" and ""with personality"" label conditions. The statistics inside the figure are unstandardized regression coefficients. The 95% confidence intervals of the indirect effects below the figure are estimated with 5,000 bootstrapped samples. Alternative explanationsWe found that ""ugly"" labeling (vs. control) did not significantly affect self-perceptions (M = 3.17, SD =. 76 vs. M = 3.31, SD =.85; F( 1, 290) = 2.13, p =.15) or any of the measures of anthropomorphic perceptions or sympathy (all ps >.3).""Ugly"" labeling (vs. control) marginally improved credibility (α =.78; M = 5.40, SD =.93 vs. M = 5.18, SD = 1.01; F( 1, 290) = 3.61, p =.06), but credibility did not mediate the effect of labeling on choice based on a 95% confidence interval (b =.09, SE =.05, 95% CI = [−.004,.188]).Images with ""ugly"" (vs. control) labels were judged funnier (r =.90; M = 2.41, SD = 1.05 vs. M = 2.02, SD =.99; F( 1, 290) = 10.54, p =.001) and more original (M = 2.65, SD = 1.05 vs. M = 2.41, SD = 1.04; F( 1, 290) = 4.25, p =.04), but not more surprising (p =.24). However, the effect of ""ugly"" labeling on produce choice was not mediated by humor (95% CI = [−.110,.039]) or by originality (95% CI = [−.046,.053]). DiscussionStudy 3 demonstrated that ""ugly"" labeling increases the choice likelihood of unattractive produce (H1) and that this effect is mediated by an increase in tastiness expectations and, to a somewhat smaller extent, healthiness expectations (H2). Unattractive produce without any specific label was judged less tasty than attractive produce, in line with past research, although unattractive produce was judged just as healthy. We return to this point in the ""General Discussion"" section. As a preview, we find across Studies 3 through 6a that people judge unattractive produce less tasty than attractive produce, but not necessarily less healthy; thus, the effect of ""ugly"" labeling on choice is mediated to a larger extent by tastiness expectations than by healthiness expectations.Naturalness expectations, credibility, self-perceptions, originality, surprise, humor, and anthropomorphic perceptions did not explain the effectiveness of ""ugly"" labeling. Study 4: Manipulating the MediatorTo confirm the causality chain tested via mediation in Study 3 (""ugly"" labeling → taste expectations; healthiness expectations → choice), Study 4 manipulated the mediator ([44]). We informed half the participants that aesthetic differences across produce do not pertain to differences in taste or healthiness. If the effectiveness of ""ugly"" labeling is due to improved taste or healthiness expectations, explicitly addressing those expectations should have the same effect as the ""ugly"" label. The study was preregistered (http://aspredicted.org/blind.php?x=br2xi3). MethodA total of 423 MTurk participants (Mage = 36.04 years, SD = 12.11 years; 54% female) were assigned to a 2 (label: ugly vs. control/no descriptor) × 2 (message: ""no other difference than visual"" vs. control/no message) between-subjects design.Participants had to choose between purchasing attractive or unattractive cucumbers. The scenario, stimuli, manipulation of ""ugly"" labeling, prices, and measurement of choice likelihood were identical to those in Study 3. In addition to the labeling manipulation, we manipulated a message such that half the participants read the following text before seeing the stimuli: ""Please be aware that although the two types of cucumbers that you will see look different, these differences in visual appearance do not pertain to any differences other than visual: for instance, they have similar gustatory or nutritive qualities.""Then, participants completed a shorter version of the attribute expectations scale: they evaluated the expected taste (tasty, flavorful, juicy, crisp) and healthiness (healthy, nutritional, full of vitamins) of the unattractive produce, relative to the attractive produce. ResultsTwenty-two participants (5.1%) failed the attention check and were excluded. Choice likelihoodAn ANOVA of choice likelihood revealed a main effect of the message manipulation (F( 1,397) = 9.50, p =.002) and a significant message × label interaction (F( 1,397) = 4.54, p =.03). The main effect of label was not significant (p =.12). When there was no message, in line with Study 3, the ""ugly"" label (vs. control label) significantly increased choice likelihood of unattractive cucumbers (M = 3.29, SD = 1.42 vs. M = 2.77, SD = 1.36; t(397) = 2.64, p =.009). However, when participants were exposed to the ""no other difference than visual"" message, the ""ugly"" label (vs. control label) no longer had a significant impact (M = 3.43, SD = 1.47 vs. M = 3.51, SD = 1.40; p =.70). In addition, comparing choice likelihood across the ""ugly"" label/no message condition and either of the two conditions in which participants received the ""no other difference than visual"" message, we found no significant differences (all ps >.30). In other words, merely labeling unattractive produce ""ugly"" had a similar effect as informing consumers that visual differences do not pertain to other attribute differences. Attribute expectationsWe created healthiness (α =.93) and tastiness (α =.93) indices and tested a moderated mediation model ([25], Model 7) with the label manipulation as the independent variable, choice likelihood as the dependent variable, healthiness and tastiness expectations as parallel mediators, and the message moderating the link between the independent variable and the mediators. The indices of moderated mediation were significant for both tastiness (95% CI = [.10,.46]) and healthiness (95% CI = [.02,.25]). The results, reported in detail in Web Appendix W8, replicated those of Study 3: among participants who did not receive the additional message (but not among those who did), the effect of ""ugly"" labeling on choice was mediated by tastiness, and to a smaller extent (and marginally significantly) by healthiness, as shown in Figure 4. DiscussionMerely labeling unattractive produce ""ugly"" had a similar effect to informing consumers that visual differences do not pertain to healthiness or tastiness differences. This provides support for our argument that ""ugly"" labeling increases choice of unattractive produce because it improves expectations about tastiness and healthiness of unattractive produce (H2). Study 5: The Moderating Effect of Price DiscountsAcross all studies presented herein, unattractive produce is sold at a 25% to 33% discount compared with attractive produce. Given the industrywide practice of discounting unattractive produce ([ 2]), Study 5 tests whether the depth of discount moderates the effectiveness of ""ugly"" labeling. We propose that ""ugly"" labels are more effective for moderate discounts because a large discount may signal low quality, thereby hindering the positive effect that ""ugly"" labels have on taste and healthiness expectations and thus on purchase (H3). The study was preregistered (https://aspredicted.org/blind.php?x=nc67z7). MethodA total of 709 MTurk participants (Mage = 35.38 years, SD = 11.37 years; 47% female) were assigned to a 3 (discount: 20% vs. 40% vs. 60%) × 2 (label: ugly vs. control) between-subjects design.All participants saw an ad for two produce boxes, described as customizable boxes of fruits and vegetables that meet USDA safety standards. The ad (shown in Web Appendix W5) depicted examples of produce contained in each of the two boxes, one featuring attractive oranges, apples, carrots, and cucumbers and the other featuring the same produce but aesthetically unattractive (we used the same photos as in Study 2). The label used for the attractive produce was always ""Fruits and Vegetables."" We manipulated the label used for the unattractive produce: either ""Ugly Fruits and Vegetables"" (""ugly"" label condition) or ""Fruits and Vegetables"" (control condition). The box with attractive produce was always priced at $20 for 5 pounds of produce. We manipulated the price of the box with unattractive produce: $16 with a ""20% OFF"" tag, $12 with a ""40% OFF"" tag, or $8 with a ""60% OFF"" tag. To facilitate measurement, the boxes were called ""Box 1"" (at the top of the ad) and ""Box 2"" (at the bottom); the position of the unattractive and attractive boxes was counterbalanced across participants.Participants indicated which produce box they would rather purchase on a five-point scale ranging from 1 = ""Definitely Box 1"" to 5 = ""Definitely Box 2,"" with a midpoint of 3 = ""I would be indifferent."" Because of the counterbalance, we reverse-coded the answers for half the participants, such that a higher number on the scale would always indicate preference for the box of unattractive produce. Then, they completed the full attribute expectations scale for the unattractive produce, relative to the attractive produce. Unlike Studies 3 and 4, this scale also included the item ""sweet (fruits only),"" which was also used to create the tastiness index.[ 6] ResultsOne hundred nineteen participants (16.8%) failed the attention check and were excluded.[ 7] Choice likelihoodAn ANOVA of choice likelihood with label, discount, counterbalance, and their interactions as independent variables showed that counterbalancing interacted with none of the manipulated factors (all ps >.57). We thus collapsed the results across counterbalance conditions and repeated the ANOVA, which revealed significant main effects of label (F( 1,586) = 4.24, p =.04) and price discount (F( 1,586) = 12.27, p <.001), and a significant label × discount interaction effect (F( 1,586) = 8.54, p =.004).As shown in Figure 5, contrast analyses revealed that the ""ugly"" label (vs. control) significantly increased the choice likelihood of unattractive produce when the price discount was 20% (M = 2.56, SD = 1.37 vs. M = 1.94, SD = 1.21; t(584) = 3.13, p =.002). When the discount was 40%, the ""ugly"" label (vs. control) had a directionally positive but nonsignificant impact on choice (M = 2.66, SD = 1.42 vs. M = 2.36, SD = 1.44; t(584) = 1.49, p =.13). When the discount was 60%, the ""ugly"" label (vs. control) had a nonsignificant impact (p =.31). Also note that ""ugly"" labeling coupled with a low discount (20%) was just as effective as providing a steep price discount (60%) with or without the ""ugly"" label (all ps >.16).Graph: Figure 5. Choice likelihood of unattractive produce box by label and price discount conditions (Study 5).**p <.01.Notes: Error bars: ±1 SE. Attribute expectationsWe created tastiness (α =.95), healthiness (α =.91), and naturalness (α =.92) expectations indices and tested a moderated mediation model ([25], Model 7) with the label manipulation as the independent variable; choice likelihood as the dependent variable; tastiness, healthiness, and naturalness expectations as parallel mediators; and discount moderating the link between the independent variable and the three mediators. Discount was treated as a continuous variable, given that the discounts increased linearly across conditions. There were significant main effects of ""ugly"" labeling on tastiness (t(586) = 3.34, p <.001) and healthiness (t(586) = 2.64, p =.009), and marginally significant label × discount interaction effects on tastiness (t(586) = −1.91, p =.056) and healthiness (t(586) = −1.95, p =.052); the other effects (including those on naturalness) were nonsignificant (all ps >.10). The indices of moderated mediation were significant for both tastiness (95% CI = [.003,.097]) and healthiness (95% CI = [.002,.080]), but not for naturalness (95% CI = [−.002,.037]). We thus do not discuss naturalness further.When the discount was 20%, the results mirrored what we found in Studies 3 and 4: the ""ugly"" label (vs. control) improved tastiness expectations (M =.36, SD = 1.34 vs. M = −.36, SD = 1.77; t(584) = 3.32, p =.001) and healthiness expectations (M =.66, SD = 1.15 vs. M =.16, SD = 1.48; t(584) = 2.64, p =.008), and, as shown in Figure 4, the effect of ""ugly"" labeling on choice was mediated by tastiness (b =.19, SE =.10, 95% CI = [.052,.424]) and healthiness (b =.13, SE =.08, 95% CI = [.023,.344). When the discount was 40%, the effects were weaker: the ""ugly"" label (vs. control) marginally improved tastiness expectations (M =.10, SD = 1.43 vs. M = −.32, SD = 1.64; t(584) = 1.90, p =.06) and healthiness expectations (M =.43, SD = 1.22 vs. M =.02, SD = 1.53; t(584) = 2.11, p =.04); the effect of ""ugly"" labeling on choice was mediated by tastiness (b =.14, SE =.09, 95% CI = [.007,.386]) but not significantly by healthiness (b =.04, SE =.08, 95% CI = [−.071,.249]). When the discount was 60%, none of these effects were significant (all ps >.54; 95% CIs include zero). DiscussionIn Study 5, ""ugly"" labeling was found to be most effective when associated with a moderate (vs. steeper) discount, in line with H3. Indeed, ""ugly"" labeling (vs. control) increased choice likelihood of unattractive produce via improved health and taste expectations when the price discount was 20%, but not when the price discount was 60%.""Ugly"" labeling allows retailers to avoid excessively discounting the price of unattractive produce: participants were just as likely to choose unattractive produce when it was labeled ""ugly"" and had a 20% discount as when it had a 60% discount (with or without ""ugly"" labeling). Indeed, while a steeper price discount naturally increases choice likelihood (as in the control condition), this was not the case in the ""ugly"" label condition. Although more affordable, produce with a 60% discount and an ""ugly"" label was expected to be less tasty and less healthy than produce with a 20% discount and an ""ugly"" label (tastiness: M = −.20, SD = 1.51 vs. M =.36, SD = 1.34, t(584) = 2.54, p =.01; healthiness: M =.27, SD = 1.35 vs. M =.66, SD = 1.15, t(584) = 2.06, p =.04). This is in line with our contention that steep discounts send a signal conflicting with the ""ugly"" label regarding produce quality. Study 6a: ""Ugly,"" ""Imperfect,"" and ""With Personality"" Labels (MTurk)In Study 6a we compare the effectiveness of ""ugly"" labeling with two other labels: ""with personality"" and ""imperfect."" ""Imperfect"" is used by numerous retailers and was the most popular label choice (beside no specific label) in our interview with grocery store managers. While this study has important practical implications, it also allows a further test of our theory that ""ugly"" labeling is most effective because it points out that the flaw in the produce is aesthetic, compared with ""imperfect"" and ""with personality"" labeling (H4). This study was preregistered (http://aspredicted.org/blind.php?x=zx2pq2). MethodA total of 440 MTurk participants (Mage = 34.78 years, SD = 11.73 years; 49% female) were assigned to one of four label conditions: ""ugly,"" ""imperfect,"" ""with personality,"" or control.The scenario, the stimuli (shown in Web Appendix W5), and the questions were similar to Study 5. However, unlike Study 5, the prices of the boxes were fixed at $18 for the box of attractive produce and $12 for the box of unattractive produce, and there was no discount tag. There were four labeling conditions for the box of unattractive produce: ""Ugly Fruits and Vegetables,"" ""Imperfect Fruits and Vegetables,"" ""Fruits and Vegetables with Personality,"" or just ""Fruits and Vegetables"" (control). ResultsForty-nine participants (11.1%) failed the attention check and were excluded. Choice likelihoodAn ANOVA of choice likelihood revealed a significant effect of labeling (F( 3, 387) = 4.40, p =.005). As shown in Web Appendix W9, the ""ugly"" label increased choice of unattractive produce (M = 2.82, SD = 1.49) significantly compared with the control label (M = 2.08, SD = 1.37; F( 1, 387) = 12.98, p <.001), marginally significantly compared with the ""imperfect"" label (M = 2.42, SD = 1.36; F( 1, 387) = 3.62, p =.058), and directionally compared with the ""with personality"" label (M = 2.51, SD = 1.50; F( 1, 387) = 2.26, p =.13).Although ""imperfect"" and ""with personality"" were less effective than ""ugly,"" they still increased choice of unattractive produce compared with the control label. The ""imperfect"" versus control contrast was marginally significant (F( 1, 387) = 2.94, p =.09), and the ""with personality"" versus control contrast was significant (F( 1, 387) = 4.43, p =.04). Attributes expectationsWe created tastiness (α =.96), healthiness (α =.93), and naturalness (α =.93) expectation indices and tested parallel mediations ([25], Model 4). The effects of the ""ugly"" label (vs. control) were in line with Studies 2–4: a significant improvement in tastiness (M = −.50, SD = 1.44 vs. M = −.97, SD = 1.36; t(387) = 2.53, p =.01), a marginally significant improvement in healthiness (α =.93, M = −.09, SD = 1.26 vs. M = −.39, SD = 1.13; t(387) = 1.83, p =.07), and a nonsignificant change in naturalness (p =.23). As shown in Figure 4, tastiness mediated the effect of ""ugly"" labeling on choice (b =.17, SE =.09, 95% CI = [.034,.394]); however, neither healthiness nor naturalness were significant mediators (95% CI = [−.035,.136], 95% CI = [−.012,.109], respectively).""Imperfect"" labeling (vs. control) did not have any significant impact on tastiness, healthiness, and naturalness expectations (all ps >.12), and none of these categories of expectations were significant mediators (95% CIs include zero).""With personality"" labeling (vs. control) positively affected tastiness (M = −.60, SD = 1.22 vs. M = −.97, SD = 1.36; t(387) = 2.05, p =.04), and tastiness mediated the effect of ""with personality"" labeling on choice (b =.20, SE =.11, 95% CI = [.013,.429]). However, ""with personality"" labeling did not significantly influence healthiness (p =.10) or naturalness (p =.70), and these categories were not significant mediators. We discuss these effects after Study 6b. Study 6b: ""Ugly,"" ""Imperfect,"" and ""With Personality"" Labels (Facebook)Study 6b compares the effectiveness of the three labeling interventions in the field through ads posted on social media platforms. We used Facebook Ads Manager's Split Test (also called ""A/B Test"") to compare the effectiveness of different versions of an ad on click-through rates, holding all other factors constant ([ 8]; [24]; [33]).As we were measuring click-throughs in advertising, rather than relative choice, we focused solely on ads with unattractive produce, and we only included ads with specific labels, namely ""ugly,"" ""imperfect,"" and ""with personality"" (i.e., there was no condition without a specific label). This study was preregistered (https://aspredicted.org/blind.php?x=rr88f8). MethodWe created an ad for a ""produce box"" of unattractive produce using the same photos of unattractive produce as in Studies 2 and 6a. The three versions of the ad each had a different label written on the box: ""Ugly Fruits and Veggies,"" ""Imperfect Fruits and Veggies,"" or ""Fruits and Veggies with Personality"" (as shown in Figure 1). We added text at the top of the ad that reinforced the label manipulation; for instance, in the ""ugly"" label condition, the text was ""Ugly fruits and vegetables delivered to your door, in a customizable box. Get 30% off your first order today."" The call to action for the ad was a button labeled ""Get Offer.""Facebook Ads Manager enabled us to determine the audience for the ad: people living in the United States, between 18 and 64 years of age, with an interest in ""Online grocer,"" ""FreshDirect,"" and ""AmazonFresh."" The ad was placed on social media platforms Facebook and Instagram, and users were randomly assigned to see one of the three versions of the ad. We programmed the campaign such that the ad would be delivered for four days, for a total cost of $600 ($200 per version). This amount was determined based on an estimated test power of 80%. Additional technical specifications appear in Web Appendix W10. ResultsOur ads were viewed a total of 42,463 times: 14,269 in the ""ugly"" condition; 14,199 in the ""imperfect"" condition; and 13,995 in the ""with personality"" condition. Thus, there was no imbalance in number of views across conditions (all ps >.17).There were 438 clicks in the ""ugly"" condition, 373 in the ""imperfect"" condition, and 404 in the ""with personality"" condition. We computed the click-through rate (CTR), defined as the number of clicks divided by the number of impressions ([33]), for each condition and analyzed the differences in CTR across conditions. As shown in Web Appendix W9, the ""ugly"" ad generated the highest CTR (3.07%) and the lowest cost per click ($.46). In line with Study 6a, the ""imperfect"" ad was the least effective (CTR = 2.62%; cost per click = $.54) and the ""with personality"" ad was in between (CTR = 2.89%; cost per click = $.50). The difference in CTR between the ""ugly"" ad and the ""imperfect"" ad was significant (χ2 = 5.04, p =.02). The differences between the ""ugly"" and the ""with personality"" ad, and between the ""imperfect"" and the ""with personality"" ad were not significant (χ2 =.82, p =.37; χ2 = 1.78, p =.18, respectively). DiscussionStudies 6a and 6b provide consistent results across very different study designs. In partial support of H4, the studies showed that ""ugly"" labeling was more effective than ""imperfect"" labeling in terms of hypothetical choice between unattractive and attractive produce (p =.058), and was also more effective at generating clicks with social media advertising in a field setting (p =.02). This is remarkable, given that the more than 50 grocery store managers that we interviewed overwhelmingly preferred ""imperfect"" labeling over ""ugly"" labeling.The ""ugly"" label was directionally more effective than the ""with personality"" label, but the differences did not approach significance (all ps >.13), failing to support H4. In addition, ""with personality"" labeling (vs. control) significantly increased choice of unattractive produce, and, as for ""ugly"" labeling, this was mediated by tastiness expectations. In retrospect, this finding may not be inconsistent with our theorizing. The label ""with personality"" is a playful reference to language that suggests someone is not attractive; thus, the label may in fact point out the aesthetic flaw, albeit in a less explicit manner. To further examine this possibility, in Web Appendix W11 we report an additional study that compares the ""ugly"" label with yet other labels: ""misshapen,"" ""inferior,"" and ""second-rate."" We found that ""ugly"" was more effective than ""inferior"" and ""second-rate,"" although ""misshapen"" was as effective as ""ugly,"" and its effect on purchase likelihood was mediated by attribute expectations. Overall, this suggests that any label that explicitly (""ugly,"" ""misshapen"") or implicitly (""with personality"") points out an aesthetic flaw may correct biased attribute expectations and increase purchase of unattractive produce. General DiscussionUp to 30% of edible produce is discarded by farmers and retailers every year because of cosmetic imperfections, contributing to the environmental cost of food waste ([ 5]). Our work offers a simple marketing communications strategy that can be easily implemented to increase the appeal of unattractive produce. Specifically, across seven experiments we show that emphasizing the aesthetic flaw of unattractive produce via ""ugly"" labeling increases purchase, choice, and click-throughs.Study 1 was conducted at a farmers' market and demonstrated that ""ugly"" labeling (vs. no specific label) increased purchase of unattractive, rather than attractive, produce. Study 2 used an incentive-compatible design and showed that ""ugly"" labeling significantly increased the likelihood that consumers use their lottery earnings to purchase a box of unattractive, rather than attractive, produce. Studies 3 and 4 showed through mediation and moderation that ""ugly"" labeling increases the choice of unattractive over attractive produce because it improves tastiness expectations and, to a smaller extent, healthiness expectations. Study 5 demonstrated that price discounts moderate the effectiveness of ""ugly"" labeling, and that ""ugly"" labeling associated with a mere 20% discount is as effective as a steep 60% discount. Studies 6a and 6b showed that ""ugly"" labeling is more effective than ""imperfect"" labeling at increasing the choice of unattractive produce and at increasing clicks on online ads. However, ""ugly"" labeling was not significantly more effective than ""with personality"" labeling (we return to this point under ""Limitations"").We theorized that ""ugly"" labeling increases acceptance of unattractive produce because it corrects for consumers' biased, negative expectations about unattractive produce. We hypothesized that this should be the case for tastiness and healthiness expectations, but not for naturalness expectations. The results on tastiness supported our theorizing: without any specific label, unattractive produce suffered from negative tastiness expectations; ""ugly"" labeling systematically corrected for these negative expectations, which mediated the effect of ""ugly"" labeling on choice. The results on naturalness also supported our theorizing. Without any specific label, unattractive produce enjoyed positive naturalness expectations. As these positive expectations are in line with fact (the absence of pesticides, preservatives, or wax coatings necessarily yields cosmetic imperfections), they did not need to be corrected, and the mediations by naturalness were never significant. The results on healthiness were more muddled, but still consistent with our theorizing. Although healthiness expectations for unattractive produce in the absence of the ""ugly"" label were never significantly negative, we nonetheless found positive effects of ""ugly"" labeling and some mediating effects, although these effects were systematically weaker than for tastiness and not always significant (see Figure 4 for all mediation analyses; see Web Appendix W12 for all means and additional analyses). Theoretical ContributionsOur research examines the effectiveness of ""ugly"" labeling, which was held constant in prior research examining how unattractive produce can negatively affect self-perceptions ([16]). In doing so, our research builds on this previous work by identifying another reason consumers reject unattractive produce: negative inferences about produce attributes. Our work also adds to research examining how food unattractiveness affects attribute expectations ([20]).We also extend the literature on ""awareness of influence"" ([49]) to the domain of consumption. In line with this literature, we show that explicitly pointing out the source of biased attitudes—in this case, produce unattractiveness—motivates validity-driven corrections of attitudes.Additionally, we contribute to research on persuasion. In the context that we study, simply adding one piece of negative information improves product evaluation. This contrasts with the literature on two-sided arguments ([42]) that has shown that weak negative information improves product evaluation, provided it is combined with positive information. However, the effects operate through different mechanisms. While two-sided arguments preempt counterarguments by explicitly addressing favorable and opposing views ([30]; [46]), ""ugly"" labeling draws consumers' attention to a nondiagnostic cue that was biasing their judgment. Limitations and Future ResearchWhile we have demonstrated the efficacy of ""ugly"" labeling, it is likely that any label pointing out the aesthetic flaw should increase purchase of unattractive produce. Studies 6a and 6b suggested that the ""with personality"" label, which hints at unattractiveness in a subtle way, was nearly as effective as the ""ugly"" label. Our study reported in Web Appendix W11 showed that the ""misshapen"" label, which clearly points out the aesthetic flaw, works as well as ""ugly"" to drive choice of unattractive produce, and both labels are driven by the same mechanism. Given our findings, it would be interesting to examine the extent to which other labels (e.g., ""misfit,"" ""pickuliar"") are perceived as pointing to aesthetics as the source of imperfection, and whether they can also motivate purchase of unattractive produce.Future research should also investigate heterogeneity in attractive–healthy associations and attractive–natural associations. While we found that people do not necessarily expect unattractive produce to be unhealthy, two studies found such associations ([20]; [47]). Looking at the stimuli used in these two studies, we suggest the possibility that when unattractiveness is operationalized with strong deformity or very unusual colors, it leads to unhealthiness inferences. Likewise, while we found that people expect unattractive produce to be more natural, research by [20] showed the opposite. This may be because Hagen's research focused on prepared and processed foods, for which cosmetic imperfections are unlikely to stem from nature. This discrepancy may also be related to measurement. Indeed, we measured naturalness with such items as ""free of pesticides"" and ""free of preservatives,"" which may activate the knowledge that a more natural mode of production results in cosmetic imperfection, while Hagen measured naturalness with such items as ""pure"" and ""unprocessed,"" which are more likely to activate notions of classic beauty. Managerial ImplicationsOur work offers significant managerial contributions: it gives clear guidance to managers on whether and how to label unattractive produce, and which price discount will maximize sales. Specifically, we show that ""ugly"" labeling is more effective than ""imperfect"" labeling and works best with moderate price discounts. Importantly, these findings largely contrast with managers' beliefs. Indeed, several large brick-and-mortar and online retailers have relied on ""imperfect"" labeling (Web Appendix W1), and the more than 50 grocery store managers we spoke to largely preferred ""imperfect"" labeling, or no specific labeling, over ""ugly"" labeling.""Ugly"" labeling can also be a support for other better-world interventions, as shown by [16] in the case of a self-esteem boost intervention. Although this has not been tested, ""ugly"" labeling may also further increase the effectiveness of more labor-intensive and costly interventions that rely on educating consumers about the environmental consequences of food waste ([ 1]; [ 6]; [53]).Online retailers who exclusively sell unattractive produce have been recently criticized for occasionally sourcing produce from industrial-scale producers, driving small-scale farmers out of business ([38]). While being cognizant of this issue, we believe that increasing consumers' interest in unattractive produce remains crucial: ""ugly"" labeling can be applied by smaller actors, particularly farmers, whose limited resources render them unable to meet the aesthetic demands and quotas required by retailers. ""Ugly"" labeling may also overcome retailers' reluctance to sell unattractive produce, whether it is because they fear a lack of consumer interest or they are concerned that steep price discounts would hurt their bottom line. Given retailers' participation in the U.S. Food Loss and Waste 2030 Champions initiative, with its objective of cutting food waste in half by 2030, our research helps reduce the uncertainty and reluctance regarding promotion of unattractive produce. In alignment with [39], which recently released a report focused on strategies to reduce food waste, our work shows how marketing can be used to shape a ""better world"" by providing a win-win solution to several stakeholders—from farmers and retailers to consumers and society at large. " 24,Genetic Data: Potential Uses and Misuses in Marketing," Advances in molecular genetics have led to the exponential growth of the direct-to-consumer genetic testing industry, resulting in the assembly of massive privately owned genetic databases. This article explores the potential impact of this new data type on the field of marketing. Drawing on findings from behavioral genetic research, the authors propose a framework that incorporates genetic influences into existing consumer behavior theory and use it to survey potential marketing uses of genetic data. Applications include business strategies that rely on genetic variants as bases for segmentation and targeting, creative uses that develop consumers' sense of community and personalization, use of genetically informed study designs to test causal relations, and refinement of consumer theory by uncovering biological mechanisms underlying behavior. The authors further evaluate ethical challenges related to autonomy, privacy, misinformation, and discrimination that are unique to the use of genetic data and are not sufficiently addressed by current regulations. They conclude by proposing an agenda for future research.","In September 2018, the music streaming service Spotify announced that it would allow its 217 million users to upload their genetic data and create playlists that ""match their genetic ancestry"" ([61]). A few months later, Mexico's national air carrier, Aeroméxico, launched a ""DNA Discounts"" campaign, offering to some customers discounted flights to Mexico, with discount rates that matched the traveler's ""Mexican DNA"" percentage, determined by a genetic test ([144]).[ 5] These actions mark the dawn of a new age, when consumers and firms alike may access information that until recently was rarely accessible: individual-level measures of the human genome.Such data are now available through the direct-to-consumer genetic testing (DTC-GT) market, whose total sales in 2019 exceeded all previous years combined. Most sales come from personalized DNA testing kits—plastic tubes that consumers spit into and then ship off for genomic analysis. The motives for taking a DNA test vary, ranging from the desire to uncover forgotten family histories to assessing genetic predispositions for diseases. As of 2020, more than 30 million people have already taken such personalized DNA tests ([120]). A by-product of the growing DTC-GT market is the accumulation of massive genetic data sets. Industry leaders, such as AncestryDNA and 23andMe, encourage consumers to participate in research by answering surveys about anything from dietary habits to personality, generating enormous data sets for investigating genetic associations to numerous outcomes. Because the sales growth of DTC-GT kits might be slowing down ([45]), DTC-GT companies are aiming to monetize their data to maintain growth. For example, Patrick Chung, a 23andMe board member, noted in an interview that ""the long game here is not to make money selling kits, although the kits are essential to get the base level data"" ([101]). In line with this notion, 23andMe has already accredited access to its data to the pharmaceutical company GlaxoSmithKline in a $300 million deal ([16]).The abundance of privately owned DNA data is concurrent with large-scale data collection efforts of public endeavors such as the UK BioBank, which genotyped nearly half a million U.K. citizens ([20]). National genome projects have also taken off in other countries, including Sweden and Singapore ([134]). The accumulation of genetic data has already fueled the discovery of associations between genes and individual differences in many traits ([91]; [100]; [143]), from dietary habits such as coffee and tea intake ([135]), to psychological traits such as adventurousness ([68]).The current research explores the potential impacts of the DNA revolution on the field of marketing and discusses possible uses and abuses of genetic data by marketers. It is organized as follows. First, we introduce key terms and review recent advances in the fields of behavioral genetics and genealogy. Drawing on these findings, we introduce a theoretical framework that incorporates genetic variables into existing consumer behavior theory. We rely on this framework to conceptually explore applications of genetic data for marketing strategy and research and evaluate under what circumstances genetic tools may be of value to marketers. We then raise ethical challenges that are unique to the use of genetic data in marketing, survey how current regulations address them (or not), and suggest potential solutions. Subsequently, we identify gaps in the current state of knowledge that must be filled to further advance the field and draw a research agenda to address them. A Primer on Human GeneticsThis section introduces basic concepts in human genetics and reviews related research that is relevant for the field of marketing (see Table 1). Our review is intended for readers who are not acquainted with the topic, and it focuses on research using DNA measures (the only type of genetic data currently available at scale). We admittedly abstract away from many subtleties and refer interested readers to other publications for more comprehensive reviews ([21]; [83]) and surveys of research using other genetic data modalities (for epigenetics, see [82]; for RNA sequencing, see [132]; for gene therapy, see [149]).GraphTable 1. Illustrative Genetics Literature of Marketing-Relevant Outcomes. The Human Genome and Its MeasurementThe human genome is a sequence of about 3 billion base pairs. There are four types of bases: adenine (A), thymine (T), guanine (G), and cytosine (C). The base pairs are packaged into structures called chromosomes and are indexed based on their location on the sequence. Every human has two copies of each chromosome, one inherited from each parent. The base pairs in most genome locations are identical across all humans and are thus not informative about interindividual variability. However, there is a small number of locations (<2%) called polymorphisms where individuals commonly differ. The most common type of polymorphism is the single-nucleotide polymorphism (SNP), which denotes locations where a single base pair differs across individuals.[ 6] For most SNPs, only two possible base pair types are observed in a given species. The more frequent base pair is called the major allele, and the other is called the minor allele. As all humans inherit one chromosome from each parent, they also inherit two copies of each SNP, and thus have either zero, one, or two minor alleles in every SNP location. This property allows for the storage of an individual's genetic data in terms of numbers of minor alleles at each SNP location (0, 1, or 2). Certain SNPs are located in subsequences of base pairs called genes. Genes shape the structure and function of every cell in the human body and are involved in many biological processes, most notably the construction of proteins ([44]). The human genome includes 20,000 to 30,000 genes.Until recently, it was extraordinarily time consuming and expensive to measure genetic variation of individuals. However, technological advances following the sequencing of the human genome by the Human Genome Project ([29]) have enabled cost-effective measurements of the genome across individuals. Common measurement techniques quantify variations in selected genome locations (typically under 1 million SNPs) where humans commonly differ. From there, around 20 million other SNPs are imputed. Twin Studies and the Three Laws of Behavioral GeneticsBehavioral genetics is a discipline dedicated to studying the relationship between genetic code and behavioral traits (also called phenotypes). Early research in the field mainly consisted of twin studies—which rely on the fact that identical (monozygotic) twins are on average twice more genetically similar to each other than fraternal (heterozygotic) twins. Under some strong assumptions ([43]), twin studies enable us to estimate a trait's heritability—the part of its interindividual variance that can be attributed to genetics. Surprisingly, twin studies have shown that most human behavioral traits are, to some degree, heritable. This finding is commonly known as ""The First Law of Behavioral Genetics"" ([138]) and was illustrated for manifold phenotypes, from psychological traits such as personality to real-life outcomes such as marital status (see Table 1). Two other empirical regularities characterize findings from behavioral twin studies. The Second Law of Behavioral Genetics states that the effect of being raised in the same family is typically smaller than that of genetics. The Third Law of Behavioral Genetics denotes that substantial behavioral variations are not accounted for by either genetics or family environment. Nonetheless, the Three Laws are not without exceptions. On the one hand, many biological phenotypes that are highly relevant for marketing of health care, nutrition, and beauty products, such as lactose intolerance (for additional examples, see Table 1) are highly heritable. The downstream behavioral consequences of these traits (e.g., the tendency to buy dairy alternative products)[ 7] are expected to be more heritable. On the other hand, various culture-related characteristics, such as one's native language or nationality, are entirely driven by the environment yet can be predicted from genetic ancestry (see the ""Genetic Ancestry"" subsection). The Three Laws demonstrate the promises and drawbacks of using measurements of the genome in marketing. Although genomes are informative about a wide range of relevant outcomes, genetic information is usually not informative for making individual-level predictions of most behavioral traits without additional variables ([60]). A unique feature of DNA data is that they are currently immutable across one's lifespan. Thus, such measures may be informative of one's future behavior long before any other variables become informative. Genome-Wide Association StudiesAlthough twin studies produce heritability estimates, they remain silent about contributions of specific genetic variants to a trait's variability. The first wave of research addressing this gap consisted of candidate gene studies—theoretically motivated examinations of associations between phenotypes and SNPs located in specific genes that were a priori hypothesized to be related to them ([77]). For example, the known role of serotonin in depression motivated studies investigating the association between depression and SNPs located on serotonergic genes ([109]). Although candidate-gene studies have yielded eye-catching findings for some applications, most in the behavioral domain have failed to replicate in subsequent studies. This failure is attributed to low statistical power, a lack of appropriate correction for multiple hypotheses testing, and a lack of control for confounding factors ([25]). Development of genotyping techniques, together with massive data collection efforts, has led to a paradigm shift from candidate-gene studies to genome-wide association studies (GWAS; [143])—data-driven investigations of the relationships between phenotypes and SNPs across the entire genome. Due to the large number of associations studied, GWAS methodology emphasizes stringent correction for multiple testing, preregistration, and replication in independent samples. Over the past decade, GWAS samples have grown from thousands to millions of participants, and the increase in statistical power has allowed researchers to identify numerous replicable associations between SNPs and behavioral phenotypes (see Table 1). However, a typical behavioral trait is associated with numerous variants, each of them accounting for a very small part (R2 <.01%) of its variance, an observation known as the ""Fourth Law of Behavioral Genetics"" ([25]).While the contribution of individual SNPs to the variability of most human behavioral traits is minute, one can obtain greater explanatory power by aggregating their effects to a polygenic risk score (PRS). The PRS is a linear combination of the most significant SNPs identified in its GWAS, and it becomes increasingly accurate as sample sizes increase. For example, a PRS constructed from a recent GWAS in 1 million people was able to predict 13% of the variance in the educational attainment in an independent sample ([80]). Polygenic risk scores are similarly informative on many behavioral traits, and firms that possess genetic data can construct them using GWAS summary statistics that either are publicly available ([91]) or can be obtained from other organizations. Yet a significant share of current publicly available PRSs of behavioral phenotypes are not accurate enough for making individual-level predictions. Furthermore, the predictive accuracy of PRSs typically decreases when applied to populations different from those used to estimate them (e.g., in ethnicity and socioeconomic status; [37]; [94]). Nonetheless, publicly available PRSs are typically computed from samples of only up to a million individuals, whereas DTC-GC companies have access to samples that are an order of magnitude larger. Moreover, advanced statistical techniques show a high potential for obtaining more accurate genome-based predictions (see the ""Advanced Targeting and Prediction"" subsection). Genetic AncestryThe possibility to quantify genetic variations in individuals has also opened up the path for studying genetic variation between populations. A common approach is to perform principal component analysis on the genetic data of a population sample in search of high-order factors that capture its variability ([ 2]). These principal components (PCs) are highly informative about one's genetic ancestry and location.[ 8] For example, a study of individuals from 51 populations worldwide found that the first PC distinguished sub-Saharan Africans from non-Africans and the second PC differentiated populations from Eastern and Western Eurasia ([84]). These findings were echoed by studies of less diverse samples that used the same method for high-resolution ancestry mapping (e.g., [106]). The PCs are also commonly used as control variables in GWAS and other population studies to account for environmental factors that vary across ethnic groups ([116]; [102]). The relevance of genetic ancestry for marketing stems from its noncausal correlation with environmental factors such as language and culture. For example, individuals of Irish ancestry are more likely to be interested in a cultural heritage trip to Ireland or celebrate Saint Patrick's Day with a pint of Guinness, and their interests may stem from cultural influences related to their ancestry. Marketers with access to genetic data may be able to infer such behavioral tendencies and the motivations underlying them and use such insight for targeting and positioning. Incorporating Genetics into Marketing TheoryThe Four Laws of Behavioral Genetics provide solid empirical grounds from which explorations of genetic effects on consumer behavior can embark. Translating these fundamental insights into applications, however, depends on incorporating them into consumer behavior theory and models. This section proposes such a framework, illustrated in Figure 1. Our theory extends the well-known stimulus-response model, which describes behavior as arising from the interaction between the organism (consumer) and stimulus ([10]). The stimulus is described via object variables, such as the products, prices, and brands offered, and situational variables, such as location, time of day, and context. The organism has traditionally been marked by personal variables denoting characteristics that are ""stable over times and places of observation and may therefore be attributed consistently to the individual"" ([10], p. 36). Typical personal variables include demographics, psychographics, and behavioral dispositions.Graph: Figure 1. A model of genetic effects on consumer behavior.Notes: Arrows represent causal relations and interactions. Dashed lines denote important noncausal correlations.Our framework extends the stimulus-response model by incorporating the elementary factors described in the previous section. We group these factors into three categories: ( 1) environment, which includes stable cultural, social, and geographical factors, as well as the flow of time influencing development and aging; ( 2) family factors, such as parenting style; and ( 3) the individual's genome, which depends on familial background, except for cases of adoption and recomposed families. Our framework considers familial and environmental factors as external to the organism, where the genome is within the organism and constitutes the most stable type of personal variables: it is fixed at conception and remains mostly stable throughout the lifespan. Our framework also extends the description of the organism by incorporating stable biological traits such as physiology (e.g., height), anatomy (e.g., brain structure), and typical brain function (e.g., connectivity between brain areas at rest). These biological traits are more directly influenced by genetics and typically mediate the influence of genetics on nonbiological personal traits. When such mediation occurs, the mediating biological trait is commonly referred to as an endophenotype.As indicated by the Three Laws of Behavioral Genetics, the environment plays a major role in the development of most personal traits. Nonetheless, genetic influences affect many outcomes of interest, starting from prenatal development and early-life stages. These effects occur via interactions with familial and environmental factors and are mediated via endophenotypes that are more directly susceptible to genetic influences, such as brain anatomy ([137]). The relative impact of genetics varies by trait. In some cases, few genetic variants have strong direct effects on a biological endophenotype (e.g., lactose tolerance; for other examples, see Table 1), and genetic data will be highly informative of their downstream consequences (e.g., interest in dairy alternatives). Most personal traits, however, are only moderately heritable and are influenced by interactions between numerous genetic and environmental factors. Importantly, the genome is also informative about characteristics that are not influenced by genetics at all, due to the noncausal correlations between genetics and environmental or familial factors (dashed lines in Figure 1). If genetic data are available, such links allow for the inference of consumer characteristics such as cultural heritage and language.The impact of genetics continues through the lifespan via two main channels. First, genetics affects later-life outcomes through its prior influence on traits that had developed earlier. For example, variants that contribute to early-life intellectual development continue to affect one's educational attainment and career in adulthood. Second, genetics continues to interact with environmental factors (e.g., time, nutrition) to influence later-life development of personal traits through biological endophenotypes such as brain anatomy and function ([131]). Although the heritability of later-life traits is typically moderate, characteristics that have a strong biological basis are well-explained by interactions between genetics and time. For example, a few SNPs explain 38% of the variance in hair loss (alopecia) in men ([111]), whose associated market size is expected to reach $3.9 billion by 2026 ([54]). These SNPs likely capture behavioral variance in this trait's downstream consequences.The final influence of genetics on consumer behaviors, such as information search, purchase decisions, satisfaction, and word-of-mouth activity, occurs through interactions with situation and object. These effects are mediated via biological processes (e.g., changes in neural activity and hormonal levels) that regulate the consumer's emotional and physiological state, as well as cognitive processes such as attention, valuation, and memory ([112]). For instance, genetics affects one's tendency to be an early riser or a night owl ([63]), and this disposition affects arousal via interaction with the time of day (situational variable) to influence behavior. Likewise, situational stressors interact with genetics to generate a person-specific stress response, regulated by activation of the hypothalamic–pituitary–adrenal axis ([46]). This response, in turn, influences decision making (e.g., [92], [93]). Genetics also interacts with object variables, as products and marketing messages may affect genetically regulated attention, reward, and valuation processes. For example, the presence of a desirable food item (e.g., in a supermarket tasting counter) elicits an appetitive (or Pavlovian) response that may increase its subjective valuation ([19]). Animal studies suggest that individual differences in this tendency, which is biologically implemented by the dopaminergic system, is partly accounted by genetic variation ([48]). Additional interactions between genetics and object occur via indirect genetic influences on heritable traits such as personality ([96]) and behavioral dispositions such as the tendency to choose the default or compromise option in a choice set ([24]; [128]). Genetic data may allow for approximating these tendencies without having to rely on large-scale customer surveys. Applications for Marketing StrategyBuilding on the framework introduced in the previous section, the following two sections discuss how the availability of genetic data may advance marketing practice and research (see Figure 2 and Table 2). We highlight that some of these applications, especially when employed by private entities in a for-profit setting, raise legal and ethical challenges (discussed in a subsequent section). It remains to be seen whether their potential benefits outweigh these concerns.Graph: Figure 2. Genetic applications for marketing strategy.GraphTable 2. Using Genetics to Advance Marketing Research. Gene-Based SegmentationWhen genetic variations correspond with consumer needs, firms may rely on genetic data to divide the market into distinct, stable, and identifiable subsets to be reached with unique marketing mixes ([49]). In some cases, genetic variants are indeed directly associated with consumer needs via known mechanisms. A firm or institution could thus rely on genetic data to identify segments that benefit from its products and services. Prior research has uncovered mechanisms that link genetic variants to phenotypes that closely map onto consumer needs in various domains (see Table 1). Most current knowledge concerns outcomes related to health care, nutrition, and beauty, with applications such as promoting screening or prevention products to individuals who are at increased risk of developing pathologies such as cancer, diabetes, or Alzheimer's disease. Indeed, leading DTC-GT companies already provide information on such risks to their consumers and aspire to use their data to become the ""Google of personalized health care"" ([101]). As genetic databases grow in size, research for nonmedically relevant causal effects is expected to increase and yield new discoveries that are relevant for marketing strategy across domains. For example, a brand manager of a product for preventing men's hair loss could rely on a specific genetic variation linked to male pattern baldness ([111]) to identify segments that are genetically disposed to alopecia. The brand manager may even be able to identify future customers long before they show any behavioral indication that they may need the product (e.g., via web searches) and increase their awareness of the brand (e.g., by advertising to males in their late 20s who are genetically disposed to baldness in their mid-30s). Using Whole Genomes to Infer Other Segmentation BasesAs Figure 1 illustrates, genetic variation correlates with almost every personal characteristic. As a result, genetic data can be used for reaching market segments when nongenetic managerially relevant variables cannot be easily observed at scale. In contrast to the direct use of specific genes as segmentation bases, most SNP associations to behavioral traits occur outside of genes, and marketers can leverage their cumulative information to infer other (nongenetic) segmentation bases. Once genetic data are available, a firm can construct for every individual in a target population PRSs that are predictive (to some degree) of every trait for which a GWAS has ever been performed ([18]). Similarly, a firm can rely on previous findings of genealogical research for calculating individual-level ancestry estimates to infer various culturally distinct motivations, interests, and behaviors. The usefulness of genomes as proxies for other segmentation variables crucially depends on how predictive they are of the target trait relative to other measures. Although genetic data might not be the most predictive of a target trait, it may be more convenient than other data sources such as surveys, which might be costly and subject to low response rates. Furthermore, adding genes to predictive models that use other variable types may improve their predictive accuracy at the individual level (see the ""Are Genes More Predictive Than Other Measures?"" subsection). Advanced Targeting and PredictionMarketers often aim to predict the probability of a single behavior (purchase, click on an ad, etc.), without necessarily understanding the underlying mechanism. As such, even a simple PRS constitutes a straightforward tool for targeting. Firms that obtain genetic data, but not samples that are large enough to estimate the coefficients used to construct a PRS, could potentially recover them from the public domain ([18]) and other organizations. More advanced statistical learning methods ([86]), including deep learning algorithms ([157]; [41]), have been adapted to genetic data to generate more accurate predictions. Furthermore, when genetic predictive estimates are available, they can be used in conjunction with other variables for early identification of consumers with high lifetime value. For instance, a coffeehouse chain may want to target consumers with a high genetic potential to enjoy espresso before they show any prior espresso-purchasing patterns in their behavioral data. While counterintuitive, such an approach would potentially allow for reaching consumers who have not yet developed an espresso consumption habit and thus are not ""locked in"" to a particular brand. This is in contrast to targeting based on more traditional variables (e.g., behavioral measures) that are likely to become predictive only after the person has already tried and developed the habit of consuming a competitor's brand.A different approach using genetic data for behavioral prediction is to consider that genomes are representative of family relations and, as such, can be used to compute a comprehensive map of relatedness between individuals. Such a map can then be used for targeting in a similar manner to social network graphs ([142]; [148]). Another possibility is to compute genetic relatedness (or inversely, genetic distance) between individuals ([117]), either for the whole genome or chromosome-wise, and leverage this metric for behavioral prediction. For instance, a company could target people who are within a small genetic distance from existing clusters of loyal customers. Methods such as collaborative filtering, nearest neighbors, or more advanced machine learning algorithms could be applied to implement such strategies ([87]). Notably, geneticists are already using similar techniques that do not depend on identifying links between specific genes and a phenotype to estimate the variance in a trait that can be explained by SNP-derived genetic distance ([151]). For example, such methods have shown that 51% of the general population variance in fluid intelligence could be explained by genetic distance, quantified from SNPs, using a sample of a few thousand people ([33]). Creative Uses: Product Development and PositioningFinally, DNA has a unique status as a ""cultural icon"" ([105]), which opens the door for creative uses, including new product development and repositioning of existing products and brands. Genetic data provide a new means of ""knowing thyself,"" connecting to previously unknown genetic relatives, and building bridges between people and their ancient family histories ([139]). Leading DT-GTC companies have created several new products and positioning strategies that translate their customers' fascination with DNA into applications that promote their sense of community and personalization. Notable examples are the aforementioned partnership between Spotify and AncestryDNA and the collaboration between Airbnb and 23andMe, which developed a service that helps travelers organize cultural experiences tailored to their ancestry. Ancestry-based positioning strategies of products and services in other domains, including entertainment (e.g., period dramas such as Downton Abbey and Braveheart), food (e.g., traditional cookbooks) and tourism (e.g., museums, heritage sites) could similarly benefit from such partnerships and creative uses. Similar strategies could employ PRS or single genetic variants. For example, most elite power athletes have a specific variant of the ACTN3 gene that encodes a protein expressed in muscle fibers ([ 3]; [79]). A sporting brand may be able to develop positioning strategies that generate a sense of community among amateur athletes who carry this variant and promote their sense of identification with brand ambassadors who also carry it. Using Genetics to Advance Marketing ResearchGenetic data can refine and substantiate existing theories of consumer behavior by illuminating the nature of relationships between traits and revealing the biological mechanisms underlying individual differences in behavior. Some of these applications are similar in nature to uses of genetics in other fields of the social sciences ([11]; [60]), where others are unique to marketing research. Estimating Causal RelationsIn many domains of consumer research, it is not feasible to study causal relations between variables experimentally. For example, experimentally studying the causal relationship between one's consumption habits and long-term happiness ([52]; [124]) would require randomly assigning individuals into groups that differ in their consumption habits or in their well-being. Such assignment could be extremely difficult for some variables and even unethical (e.g., if a group is required to worsen dietary habits, creating a threat to their health). Furthermore, studying such causal relations using observational data is also not straightforward. First, many personal and environmental factors (e.g., socioeconomic status, personality) confound the relationship between the explanatory variable and outcome. Second, there exists a possibility of reverse causality.Sometimes, it is possible to overcome the aforementioned limitations using instrumental variables (IVs; [ 4]). Instrumental variables are factors that cause changes in the explanatory variable of interest (e.g., consumption habits) and have no other independent effects on the outcome (e.g., happiness), enabling one to estimate the causal effect without bias due to confounds and reverse causality. Under some circumstances, genetic measures can be used as IVs. This is possible because the transmission of genetic variants from parents to offspring is determined via a ""genetic lottery"" that is independent from environmental factors (conditioned on the parents' genomes). Furthermore, because genetic variations are not influenced by one's environment or habits, reverse causality is not a concern.The most common method that uses genetic measures as IVs is Mendelian randomization (MR; [130]), which can be thought of as a natural experiment that occurs at the time of conception. Mendelian randomization uses genetic variants that have well-established causal influences on the explanatory trait as IVs to quantify the trait's causal effect on an outcome. For example, medical researchers have been using variants that regulate alcohol metabolism as IVs for studying the long-term causal effects of alcohol consumption on outcomes such as cardiovascular disease and cognitive decline (e.g., [26]). When using genetic data to infer causal relations, it is important to keep a careful eye on the assumptions of the methods used to estimate the effects. One crucial issue is that the transmission of genes occurs at random only within a family. Therefore, MR studies should ideally rely on within-family designs that compare genetic variation between related individuals (e.g., sibling pairs, parent–offspring trios). Mendelian randomization studies that do not use such designs are susceptible to biases of various sources ([34]). A second important assumption of MR is that the genes used as IVs affect the outcome only via their effect on the explanatory trait (a criterion called ""exclusion restriction""). It is therefore important that the mechanisms linking the genetic IVs and the explanatory variable are well-understood, and that the genes' prevalence in the population studied does not correlate with unobservable environmental factors that might influence the outcome ([74]).One limitation of MR is that most genetic variants are relatively weak instruments, because their associations with personal traits of interest are small. Moreover, genetic variants typically correlate with multiple traits that could influence an outcome, a phenomenon called ""pleiotropy."" Several statistical techniques that rely on summary statistics from large-scale GWAS (instead of single variants) have been recently proposed to overcome these issues ([36]; [108]; [156]). Each of these methods relies on a different set of assumptions concerning the relationships between genetics and other variables that are included in (or omitted from) the model, for estimating a causal effect. To mitigate concerns that claims of causality are driven by any specific assumptions, it is crucial to verify that a study's conclusion is consistent across methods. We anticipate that continuing development of such methods, together with the growing availability of data sets that include genetic measures of related-individuals, will provide a fertile ground for investigations of causal relations for a broader range of settings in the near future. Accounting for Otherwise Unobserved Heterogeneity using PRSsGenetic variation between individuals is fixed across the lifespan and can be related to many outcomes of interest to consumer researchers. As such, including genetic variables (most notably PRSs and genetic PCs) in statistical models that quantify any nongenetic effects provides a means to control for unobservable factors that would otherwise be a part of the model's error. Such reduction of the model's error would increase the study's statistical power and allow estimating model parameters of interest with less uncertainty ([11]). For illustration, consider a field experiment aiming to test the efficiency of different campaigns for preventing smoking initiation among teenagers. In such settings, PRSs can explain one's genetic tendency to smoke, as well as variance related to many preexisting personal characteristics that are not contaminated by the treatment and could be related to future smoking (e.g., extraversion). Including such PRSs in the model would therefore allow for quantifying the treatment effect more accurately. Studying Person–Object and Person–Situation InteractionsGenetic measures are also useful in studying how consumers differentially respond to marketing stimuli or situational contexts. As noted previously, generic variants per se are not of great interest to marketers, but they allow for calculation of PRSs (based on any previously published GWAS) to approximate personal characteristics that cannot be easily measured in large samples (e.g., intelligence, personality) or when the participants' tendencies are not yet expressed behaviorally. Going back to the smoking-prevention field experiment example, constructing PRSs for many unobservable traits in the sample could be used for carrying a post hoc analysis to investigate whether certain individuals more strongly respond to a certain treatment versus another. Studying Relationships Between Traits Using Genetic CorrelationsBecause genetic variation correlates with many personal characteristics, it provides a means for studying the relationships between traits and whether they arise from genetic or environmental causes. A useful method for quantifying the genetic overlap between traits is estimating their genetic correlation (rg), which measures the amount of variance they share due to genetic causes ([90]). A useful feature of genetic correlations is that they can be estimated between any two traits for which GWAS has ever been conducted—even for traits that have not been measured in the same sample ([17]). A recent example for insight obtained from genetic correlations comes from a GWAS of general risk tolerance in a sample of over 1 million people (Karlsson [68]). This study found that the genetic correlations between general risk tolerance and many domain-specific risky behaviors—such as substance use, speeding on motorways, and self-employment—were substantially larger than the correlations observed between the behavioral phenotypes. This finding indicates that common genetic causes influence all these phenotypes, where the translation of this genetic tendency to each of the domain-specific risky behaviors depends on environmental factors. Identifying Biological MechanismsGenetic data can enrich marketing theory by illuminating biological mechanisms that underlie behavior, akin to research in the field of consumer neuroscience ([113]). Apart from straightforward genetic effects on traits like lactose intolerance, genetic analyses can provide insight into how different brain systems mediate the influence of genetics on complex behavioral traits, such as economic preferences and consumption patterns. Although brain imaging studies have long ago uncovered multiple systems that are functionally involved in emotional and cognitive processes, linking functional brain measures to differences across individuals is not straightforward, because of their low test-retest reliability ([39]) and the high cost of obtaining such measures at scale. Genetic variation, in contrast, can be measured reliably and inexpensively in large samples, and once genetic variants are linked to a behavioral trait, they can be tied to neurobiological systems via bioinformatic tools (e.g., [47]). For example, the recent GWAS of general risk tolerance pointed to multiple brain systems that are genetically associated with the trait, including the prefrontal cortex, the amygdala and mid-brain regions involved in reward processing ([68]). An alternative promising approach is to derive biologically informed PRSs, which reflect aggregate effects of variants related to known biological systems (e.g., the dopaminergic genes) on a target phenotype, and investigate their relationship with biological endophenotypes ([32]). The rapid development of bio-annotation techniques, together with the formation of data sets that include both genetic and brain-imaging measures ([ 6]), will facilitate additional discoveries of gene-brain-behavior pathways in the near future. Ethical and Legal ChallengesSimilar to other data types, some marketing uses of genetic data can improve individuals' well-being and have a positive impact on society as a whole. For example, focused early interventions based on genetic data may help health care providers reach patients at high risk for conditions such as diabetes and hypertension and provide them strategies that mitigate these risks (e.g., via physical exercise and diet; [146]). However, genetic data might facilitate manipulation and exploitation of vulnerable individuals ([133]). For example, e-cigarette companies could use genetic data to target teenagers who are more genetically prone to develop nicotine addiction ([121]). Yet the use of human genetic data by marketers raises even further ethical and legal challenges. These issues are the result of several unique features of genetic data, which contain immutable and identifiable information that is predictive of future behavior and disease, both for the individual and their genetic relatives. For this reason, genetic data have been considered particularly sensitive even within the medical field, a view known as ""genetic exceptionalism"" ([56]). In this section, we highlight serious ethical issues that emerge from these unique properties, review the current state of legislation in this area, and propose possible solutions. Identifiability and Informed ConsentExcept for monozygotic twins, genetic data can be uniquely attributed to one person: A mere 60 to 300 randomly selected SNPs are sufficient to identify an individual ([154]). Anonymizing genetic data without destroying a large share of the information is not a simple task. Some methods, for instance, try to balance anonymity and information preservation by clustering the data before analysis ([88]). Even when the data are labeled as anonymized, however, the inherent information they contain could allow for potential reidentification attacks ([150]). Due to the combination of this unique identifiability property and the rich information content of genetic data, using them for research requires obtaining informed consent from study participants ([12]). Nonetheless, even in the ethically stricter research setting, acceptable anonymization and consent practices have been subject to heterogenous standards ([38]).Because most current human genetic research involves analysis of secondary data that have been typically collected long before hypotheses are formed, obtaining consent is challenging. A common solution has been to ask participants to consent for all future research that falls within a broadly defined scope. For example, 23andMe informs customers who volunteer to participate in research that ""the topics to be studied span a wide range of traits and conditions"" and that ""some of these studies may be sponsored by or conducted on behalf of third parties.""[ 9] Similar consent procedures are used in practice by other DTC-GT firms and biobanks. Advocates of the broad consent approach argue that it provides an ideal trade-off between participants' autonomy and the public interest to benefit from research outputs ([59]). However, it is unclear whether genetic research subjects can fully appreciate the potential benefits and risks of any future research at the time of consent. For instance, it is unlikely that 23andMe customers could foresee that access to their data would be sold to a pharmaceutical company under the broad label of ""research."" To overcome these issues, scholars have proposed using dynamic or hybrid consenting protocols, where individuals can opt in to studies or withdraw their consent online ([71]; [114]).To complicate matters further, one's genetic data are informative not only about oneself but also about one's nongenotyped relatives. This issue was recently illustrated in the apprehension of Joseph James DeAngelo, the alleged Golden State Killer, who was arrested after a fraction of his genome could be matched to the DNA of distant relatives, who uploaded their genetic data to a searchable public genealogical database ([118]). Although relatives of genetic research participants are potentially identifiable, current guidelines do not require obtaining their consent yet recommend that participants consult relatives when deciding to take part in research ([98]). These guidelines may change in the future, as genetic identification technology advances.In summary, several unique issues make it difficult to anonymize data and obtain fully informed consent from participants of genetic research. Because this is an active area of study, we recommend that researchers closely monitor the emerging literature on the topic and ensure that their studies comply with the latest ethical guidelines. It is imperative that analyses of publicly available genetic data, collected thanks to public funding, produce discoveries that benefit society as a whole. As for research using privately owned genetic data, it is crucial that informed consent is obtained and that all studies fall beyond a shadow of doubt under the scope of research to which participants had consented. Privacy and SecurityMany of the features that turn genetic data into a marketing opportunity also raise fundamental privacy and security challenges. Genetic data are identifiable, predictive of virtually every aspect of one's life, and are even informative about one's relatives—and thus, could enable firms to target individuals who never opted to share any information. Given that major companies have been known to keep ""shadow profiles"" of individuals who did not register for their services ([50]), this potential privacy threat is imminent. The assembly of privately owned genetic databases also gives rise to security concerns, as major data breaches become increasingly common ([27]). In these cases, third parties obtain data against the will of both the consumer and the data holder. Once leaked, data will likely be used regardless of any regulation or ethical norm.As massive volumes of genetic data reside on the servers of private firms, the question arises as to whether legislation and practice sufficiently protect the privacy of consumers from having their data exploited against their interests. While leading DTC-GT companies argue that their research complies with ethical guidelines, and they have a clear interest to avoid public controversies, it is unclear whether they follow the same principles when using data for marketing. As of July 2020, market leader 23andMe indicates in its (unilaterally modifiable) privacy statement that it would not process genetic data for marketing purposes without explicit consent, implying that it may do so if consent is given.[10] Furthermore, ethical recommendations are likely not a priority for all entities that own genetic data. In the absence of legal regulation and transparency, it becomes difficult to know exactly how private companies use the data.Surprisingly, current federal laws in the United States concerning the use of genetic data have little implications on the DTC-GT industry, and U.S. lawmakers have mostly remained silent (with some notable exceptions) regarding potential regulations on the use of genetic data. As a consequence, the license to use and share genetic data for marketing purposes depends on the privacy policy of each individual company. Currently, a large number of U.S.-based DTC-GT companies do not provide their customers with any privacy information (on their website or the testing-kit packages) prior to the purchase of DNA kits, and the policies of many of the remaining companies indicate that they may use genetic data for purposes other than delivering ancestry and health reports. Furthermore, companies often reserve the right to share genetic data with third parties in cases of merger, acquisition, or bankruptcy, or to modify their privacy policies without notification ([62]).In contrast to U.S. federal law, the recent European General Data Protection Regulation, commonly known as GDPR, explicitly recognizes genetic data as ""sensitive"" under Article 9 ([125]) and provides unique protection against sharing of genetic data (even semianonymized). Under current European regulations, one has to provide ""explicit consent to the processing of personal data for one or more specified purposes.""[11] Nonetheless, consumers have been known to easily approve mining of their data without reading the legal terms and services conditions ([107]). Once such consent is provided, virtually every marketing application becomes possible, despite the strict sharing restrictions in place. Furthermore, DTC-GT companies can process genetic data and use them for running marketing campaigns on behalf of other companies, without having to directly share them. For example, DTC-GT companies can offer to forward a message to a subsample of their clients satisfying some criteria on behalf of other entities, without disclosing any data, just as Facebook allows advertisers to target its own users without sharing their data ([96]). Thus, regulatory limits to genetic data sharing may end up simply granting DTC-GT companies a monopoly over the data. Finally, it is important to recognize that the power of regulation might be limited. Industry practices typically advance faster than the policies trying to regulate them, with regulations doing too little too late after malpractice had already been exposed ([64]). Moreover, technology giants have a long history of violating data protection laws and do not appear to be deterred by financial disincentives, as indicated by numerous condemnations and legal battles between regulatory agencies and these entities.A possible solution to the privacy and security issues—which, in our view, is crucial for the continuing growth of the DTC-GT market—is adoption of industry standards that guarantee acceptable practices. One such framework, previously proposed to address similar challenges in artificial intelligence (AI) research, can be directly applied to genetic data ([136]). This framework, namely the ""Four Pillars of Perfectly Privacy-Preserving AI,"" articulates four principles for maintaining privacy, security, and usability of data: ( 1) training data privacy: a malicious actor will not be able to recover genetic data from other accessible information (e.g., model output); ( 2) input privacy: a user's genetic data should not be observed by other parties, including the model creator; ( 3) output privacy: the output of a model should not be visible by anyone except for the user whose data are being analyzed; and ( 4) model privacy: the model (trained or not) should be protected from being stolen by a malicious party.A specific strength of this framework is that privacy is considered from both sides. From the consumer side, data and the inferences (e.g., genetic reports, ads selected for the consumer) are not visible to the company. From the company's side, its algorithms and parameters (e.g., GWAS weights) are not visible to the consumer. Importantly, no data have to be kept on the consumer side if a sufficiently strong encryption algorithm is applied to them before delivery to third-party servers. Thus, the consumer would only need to preserve the encryption key.While algorithms satisfying some or all of the aforementioned criteria are still under active research, several methods to perform privacy-preserving GWAS already exist ([65]; [141]; [153]). With these methods, the GWAS's summary statistics (e.g., weights, p-values) are known to the analyst, yet the PRSs can only be computed on the consumer side. Concurrently, general methods to allow for privacy-preserving versions of machine learning algorithms are being developed and can be expected to be adapted to genetic data mining, following their nonprivacy preserving counterparts ([85]; [127]). An additional advantage of such methods is that users can withdraw their data from the pool unilaterally by deleting either the data or the encryption key. However, even though such methods are constantly being developed, it is far from clear whether companies will end up adopting them. Major industry actors might not feel compelled to change their practice without strong incentives. A possible solution would be to enforce the use of these technologies through regulations. We can, for instance, picture a legal framework wherein a DTC-GT kit cannot be sold in a country without adhering to a framework of this type. MisinformationIn November 2013, the U.S. Food and Drug Administration ordered 23andMe to suspend its genetic health reporting service until the company provided sufficient evidence to support clinical claims made in its reports. The company, which at that time had already sold half a million kits, relaunched the service only two years later, with less elaborate reporting that emphasized the probabilistic nature of genetic diagnosis ([115]). Although regulation of health-related genetic applications has tightened up, this cautionary tale illustrates how companies might use the scientific image of genetics in their consumers' minds to oversell the utility of genetic information. When doing so, marketers could rely on genetic data to make pseudo-scientific claims that promote the appeal of products and services, as commonly done in the wellness industry ([ 7]).In the United States, because nonmedical genetic applications do not pose direct health risks to consumers, the Federal Trade Commission, rather than the Food and Drug Administration, is responsible for regulating potentially deceptive marketing messages that make claims on the utility of genetic data ([70]). However, such oversight might be difficult to exercise, for three main reasons. First, although genetic data are only moderately informative of most human behavioral traits, they do indeed contain some information. As a result, it is difficult to argue that genetic-based recommendations are entirely deceitful. Second, consumers' perceptions of genetics ([155]) might make them prone to believe that genetic-based recommendations are always backed by scientific evidence, even when such claims are not made explicitly. Third, people have a poor intuitive sense of probabilities and thus might be prone to overestimate the informativeness of genetic-based recommendations even when their probabilistic nature is communicated ([140]). In our view, regulation should ensure that companies disclose the science underlying any scientific claims (and its limitations), attempt to communicate probabilistic information intuitively, and avoid the use of deterministic language when appropriate ([147]). DiscriminationSimilar to discrimination based on other unchangeable characteristics, negative treatment of individuals based on their actual (or assumed) genetic markup is a potential source of distress, exclusion, and loss of opportunities ([14]). Furthermore, such discrimination might deter individuals from taking genetic tests that could improve their health care or from participating in genetic research that benefits society as a whole ([67]). To date, most conversations concerning genetic discrimination among ethics and law scholars have focused on potential abuses of genetic data by insurance providers and employers ([66]; [81]). Yet marketing applications of genetic data give rise to similar concerns. Aeroméxico's aforementioned DNA-discounts campaign is a recent prominent example of what is essentially genetic-based price discrimination. While it is unclear whether customers indeed received DNA discounts, the campaign was covered by major popular media outlets and, in general, was received positively by the public.From a legal standpoint, the 1996 Health Insurance Portability and Accountability Act and 2008 Genetic Information Nondiscrimination Act prohibit insurance companies (for specific types of policies) and employers from discriminating against people based on genetic information, but they do not protect individuals from discrimination in other circumstances. However, some state laws, most notably California's Unruh Civil Rights Act, explicitly ban businesses from discriminating against consumers based on genetic information. Similarly, Florida statutes have provisions requiring notification of an individual if genetic information was used in any decision to grant or deny any insurance, employment, mortgage, loan, credit, or educational opportunity. In our view, discrimination based on one's genetic information is a serious issue that should be addressed in the same way as other types of discrimination. Self-Reinforcing LoopsA final nontrivial concern is that marketing strategies that rely on consumers' genes for predicting their preferences and behavior might generate self-reinforcing loops ([55]) that perpetuate inequality and deprive consumer's exploration of options that do not align with their genetic markup. For example, providers of SAT preparation kits could offer promotions to high school students who are genetically disposed to higher education ([80]) and, by doing so, give preferential treatment to individuals who are already in an advantageous position. Open QuestionsForthcoming discoveries in the field of behavioral genetics will undoubtedly advance our understanding of how genetics interacts with the environment to influence behavior. However, assessing the utility of genetic tools for the advancement of marketing theory and practice, and accurately evaluating the severity of ethical concerns, would require addressing several gaps of knowledge in the current literature (summarized in the Appendix). Unveiling the Genetic Underpinnings of Consumer BehaviorMany genetic associations of phenotypes that are of interest to marketers have been identified over the past decade. Nonetheless, the genetic underpinnings of many traits that are more closely tied to consumer behavior and are known to be heritable (see Table 1) have remained elusive. There are two likely reasons for this gap. First, marketing scholars have largely neglected the influence of genetics on consumer behavior (with a few notable exceptions, e.g., [128]). Research in related fields, however, points to genetic effects on many traits that are central to consumer behavior theory and practice. Examples include investment decisions ([23]; [31]), altruism and trust ([22]; [110]), susceptibility to placebo effects ([58]), voting turnout ([89]), and mobile phone usage patterns ([99]). Molecular genetic studies of these traits would be a straightforward extension that can enrich marketing theory and support industry applications.Second, genetic data sets that include fine-grained behavioral measures are scarce. Behavioral geneticists have overcome this limitation by using measures that are more readily available at scale as proxies for traits that are laborious to measure, an approach that was shown to boost statistical power of genetic discovery ([122]). Genetic research of consumer behavior can similarly benefit from such an approach. For example, a twin study found that one's disposition to display decision biases shares a common genetic variance with performance in the cognitive reflection test ([24]), suggesting that this brief measure could serve as a proxy for such behavioral dispositions. Another possible solution would be to preselect genetic loci that have already been identified as associated with related phenotypes in large-scale GWAS. This would drastically reduce the number of hypotheses to be tested and, thus, the sample size required to obtain sufficient statistical power.On a final note, the capacity (or lack thereof) to obtain detailed phenotypic measures at scale to complement the genetic measures may be less of a concern for behavioral marketing metrics. Companies in the DTC-GT industry possess relationship management data of millions of customers and likely know whether they were early adopters, responded to email advertisements, shared coupons with their friends, and consulted health or ancestry reports and, furthermore, what device was used to access them. Thus, large-scale genetic data sets that contain high-resolution measures of consumer behavior already exist and could be employed to unveil the genetic foundations of many aspects of consumer behavior. Such explorations will generate insights that advance not only the field of marketing, but also the discipline of behavioral genetics. Are Genes More Predictive Than Other Measures?Behavioral genetic research typically focuses on identifying variants that have causal effects on a target trait and quantifying the variance they account for. Many marketing applications of genetic data, however, do not depend on whether genetic variants are indeed causally related to a trait but, rather, on whether they are more informative than other readily available measures. These two questions are not interchangeable for two reasons. First, genomes correlate with many personal characteristics that have no genetic basis. In traditional genetic analysis, noncausal correlations are of no interest ([116]). For marketing applications, however, noncausal genetic associations carry information that is useful for identifying segments and reaching targets. Second, many behavioral dispositions can be accurately predicted from records of their downstream consequences (e.g., personality can be estimated from digital footprints; [75]; [102]). This empirical observation is not of particular interest to geneticists, yet it is crucial for marketers deciding on what data to base their strategy. As noted previously, the degree to which genomes are more predictive than other measures likely varies by trait. To the best of our knowledge, only one study to date (whose outcome measure was longevity) systematically compared the predictive accuracy of models that use different sets of variables ([69]). This constitutes an important gap that should be filled as genetic data sets become more available to marketing researchers. Extreme Ends of DistributionsMarketing applications, such as segmentation and targeting, often depend on identifying people at the extreme ends of a trait's distribution as opposed to explaining the variance in the general population. For example, a manager of an eco-friendly luxury car brand would be interested in reaching people who are willing to pay a lot for ""green"" products ([78]) rather than accounting for heterogeneity in this tendency in the general population. However, the goal of most behavioral genetic research to date has been to estimate how much of a trait's variance in the general population can be attributed to genetics (using summary statistics such as estimated heritability or R2). Future studies should shed light on the capacity to use genetic data for identifying segments at the extreme ends of the behavioral distribution, using techniques such as discriminant analysis. When We Do Not Have Genetic Data for EveryoneAs described in the ""Applications for Marketing Strategy"" section, there are several cases when a marketing researcher can use genetic data to accurately predict a variable of interest, denoted by y (e.g., propensity for pattern baldness). However, genetic information might not be available for the entire population of potential consumers. In such cases, it may be possible to leverage the share of the population with genetic information to predict y for the remaining nongenotyped population. To this end, researchers must first predict the variable of interest in the population using genetic information (e.g., using a genetic estimator y′, such as a PRS) and then estimate a model to capture the link between nongenetic variables (e.g., demographics) and the predicted variable of interest y′. Finally, the model can be used to predict the variable of interest in the nongenotyped population, without having to rely on genetic data. The feasibility of this approach crucially depends on the capacity to estimate y′, which is a function of the genome, from other observables. We are not aware of any research relying on this approach to date, and its potential performance remains to be studied. Answering this question is crucial for evaluating the utility of genetic variables as segmentation bases. How Will Consumers React?A final important open question concerns how consumers would feel about the use of their genetic data by marketers. On the one hand, it seems plausible that at least some people would welcome marketing applications of genetic data if it got them discounts or better recommendations, which saves search costs. On the other hand, such usage is expected to raise privacy concerns that are similar to those invoked in relation to other data types. Yet there are several additional unique matters, related to the image of genetics in the minds of consumers. One major concern relates to historical misconceptions surrounding genetics, which were used in the past to justify racist worldviews and policies responsible for some of the worst crimes against humanity ([72]). Business strategies that insensitively use consumers' genetic data might therefore invoke strong negative reactions. Furthermore, although the true causal effects of genetic factors on most human traits are moderate in size and occur via interactions with the environment, genetics is often associated with biological determinism ([30]). As such, the use of genetics for matching consumers with products, services, and ads might increase beliefs in the existence of potentially deterministic aspects of behavior ([13]; [155]) and threaten consumers' sense of autonomy ([145]). A final concern is that an individual's genome contains sensitive information that they may not be aware of, for example, about future health risks such as cancer or Parkinson's disease. Marketers should be cautious to avoid exposing consumers to information they might not want to know ([51]) or prefer to receive with the appropriate counseling in a medical setting.The substantial size of the DTC-GT market, despite poor regulation, suggests that these issues may not be a major concern for many customers. Moreover, many individuals voluntarily share their genetic data with third-party interpretation services ([57]) and websites that use them solely for making product recommendations. Indeed, in addition to ancestry-based playlists (Spotify) and cultural experiences (Airbnb), other services have recently emerged, recommending wines, travel destinations, and even romantic matches purportedly tailored to their consumers' DNA. Yet it is possible that the market trends merely reflect consumer ignorance. A recent survey found that while many DTC-GT customers presumed that they were sufficiently informed about privacy issues, their expectations were often inconsistent with company practices ([28]). For example, consumers' most common expectation, that DTC-GT companies would not share their data with third parties, was often at odds with the firms' actual privacy policies. Thus, it remains to be seen whether consumers' attitudes toward the use of genetic data for marketing differ from how they (dis)regard the use of other types of digital records, and whether there are means to mitigate such effects (e.g., by increasing transparency; [73]). ConclusionThis article is a first attempt to assess how the massive amounts of data accumulated in genetic databases will influence the field of marketing. We developed a framework that incorporates genetic variables into consumer behavior theory and used it to explore potential applications of genetic data in marketing. We further evaluated ethical and legal challenges, and we highlighted gaps of knowledge that should be addressed by future research. Despite the gaps of knowledge in the published literature, we note that DTC-GT firms and governments already have access to the genetic data of millions of individuals. Therefore, business strategies that employ genetic data are likely already implemented, to some degree, by organizations. With the fast accumulation of genetic data and the rapid advances in methodology for genetic-based inference, the use of genetic data for marketing research and practice is likely to become increasingly common in the future. Appendix Directions for Future Research Which specific genetic variants are linked to relevant marketing outcomes (e.g., customer relationship management measures)? To what extent is genetic data predictive of consumer behavior, above and beyond nongenetic variables traditionally used in marketing? Do genetic data allow identification of individuals at the extreme ends of distributions (e.g., heavy espresso drinkers)? To what degree can genetic variation be approximated from nongenetic measures traditionally used in marketing (e.g., geodemographics)? How will consumers react to the use and monetization of their genetic data by marketers? " 25,Gimmicky or Effective? The Effects of Imaginative Displays on Customers' Purchase Behavior," Prior research indicates the strategic importance of the store environment in enhancing customers' shopping experience and their purchase decisions. This article examines the effects of imaginative displays on customers' purchase behavior. An imaginative display is constructed using multiple units of the same product in a novel or innovative yet aesthetically appealing form, which could be themed (i.e., having a particular shape mimicking an object) or unthemed. Six studies in both lab and field settings show that, relative to standard displays (i.e., non-novel and neutral aesthetics), imaginative displays can increase customers' purchase behavior and intentions. Importantly, for themed imaginative displays, these effects work through the dual mechanisms of affect-based arousal and cognition-based inferred benefits, which are contingent on congruence between display form and perceived product benefit. Findings from this research not only contribute to the literature on in-store display and store atmospherics but also have significant practical implications for retailers. Specifically, while imaginative displays may appear gimmicky, they can favorably influence customers' purchase behavior and increase product sales at relatively low costs.","Retail atmosphere, whether established in-store or through various retail touch-points, can influence customers' product choices and perceptions of the retailer ([59]). For instance, strategically altering the store environment can enhance customers' shopping experience and their purchase behavior ([ 1]; [33]). In particular, judicious use of in-store displays can influence customers' behavior and increase retailers' sales (e.g., [14]; [60]). Companies such as Coca-Cola often create novel and aesthetically appealing displays in stores, which have been shown to increase store sales (see the Web Appendix).A survey of 2,400 supermarket shoppers indicates that half of them recalled seeing at least one display during their shopping trips, with endcap and freestanding displays being the most prominent; importantly, this survey also shows that one in six purchases were made when a display was present in store ([54]). Moreover, displays are particularly useful in generating unplanned purchases of regularly purchased product categories, with an increase of almost 40% from the baseline level ([32]), and are even more effective than temporary price reductions ([49]).In this vein, prior research has examined two important facets of in-store display: display form ([12]) and display context ([72]). ""Display form"" refers to how the products are displayed (e.g., [17]; [50]), while ""display context"" refers to contextual cues surrounding the display (e.g., [22]; [72]). In extending the literature on display form and display context, the present research examines the effects of imaginative displays that are both novel (i.e., innovative) and aesthetically appealing on customers' purchase behavior. We consider design novelty and aesthetics two critical elements of imaginative displays, as customers tend to be attracted to new things and their response to the new display form is influenced by visual impression of the display ([55]). The visual design of a novel stimulus enables a retailer to gain customers' attention and stand out from its competitors ([ 7]; [56]).Specifically, we investigate how imaginative displays that are novel and aesthetically appealing increase customers' purchase behavior compared with standard displays (i.e., non-novel and neutral aesthetics). To this end, we tap into the literatures on in-store display (e.g., [ 1]), innovativeness (e.g., [55]), and aesthetic design (e.g., [30]). We reveal two mechanisms underlying the effects of imaginative displays: affective response of arousal and cognitive response of inferred product benefits. Finally, we identify (in)congruence between inferred benefit from the display form and perceived product benefit as a contextual cue that moderates these core effects.We conduct six empirical studies. Studies 1 and 2 are field experiments that show the effects of imaginative displays on actual purchases. We then test the underlying mechanism of arousal using two complementary designs, by measuring arousal (Study 3a) and by manipulating arousal (Study 3b). Study 4 tests the dual mediating effects of arousal and inferred product benefits from themed imaginative displays (i.e., with particular shapes mimicking actual objects). Finally, Study 5 tests the moderating role of congruence between display form and perceived product benefit on the core effects. Figure 1 presents the conceptual model.Graph: Figure 1. Conceptual framework.In the following sections, we first review the relevant literature and develop the hypotheses shown in the framework. Next, we report empirical results from six studies designed to test the hypotheses. Finally, we discuss the theoretical and practical contributions of our research, as well as its limitations and future research directions. Theoretical Background and Hypotheses Development In-Store Display Form and Display ContextConsiderable research examines the effects of in-store displays on customer behavior ([12]; [14]; [32]). A key stream of research examines the effects of display form on customer responses, including display direction ([17]), display formation ([50]), space-to-product ratio of the display ([63]), display organization ([12]), shelf scarcity ([52]), digital display ([60]), display completeness ([57]), and online virtual display ([10]).Another research stream suggests that customers' evaluations of the product on display are also influenced by the display context, such as scent in the shopping environment ([22]), coordination of product grouping ([36]), surface material of the tablecloth ([72]), spatial layout of the display stand ([13]), and assortment organization ([61]). The present research extends both streams by examining the dual effects of novelty and aesthetics of imaginative displays on customers' purchase behavior. Table 1 summarizes the positioning of the present research vis-à-vis prior research on in-store displays.GraphTable 1. Positioning of Present Research in the In-Store Product Display Literature. Imaginative DisplayThe product display form consists of a number of elements that retailers choose and blend into a whole to achieve a particular sensory effect ([ 7]; [31]). Prior research suggests that an effective product display design should be perceived as innovative and visually appealing ([55]). We conceptualize an imaginative display in terms of the degree of deviation from the prototypical category exemplar of physical appearance and visual attractiveness of the design. Specifically, we define an imaginative display as a product display constructed using multiple units of the same product in a novel yet aesthetically appealing form. Thus, an imaginative display combines the two critical elements of novelty and aesthetics to achieve optimal visual effectiveness. The novel element is operationalized via the innovative and unusual appearance of the display compared with the prototype ([48]), while the aesthetic element reflects its ability to please the visual senses ([ 8]). Note that our definition of an imaginative display excludes other forms of product displays not constructed using multiple units of the same product (e.g., inflatable displays).In marketing, ""novelty"" usually refers to a stimulus that is unfamiliar to the consumer ([29]) and reflects a comparison of the object with previous versions in the same or proximal categories ([55]). According to categorization theory, consumers' repeated exposure to seeing different products in the same category would lead them to develop a prototype consisting of the average value of the design features of that category (""central tendency""; [ 2]), which becomes representative of the product category ([69]). Product appearances that significantly deviate from (vs. resemble) the prototype are more novel or innovative ([48]). Moreover, perceived novelty of a stimulus not only can be due to its inherent characteristics but also could be contextual, such as when contrasted against other nearby elements ([34]).Prior research has examined consumers' responses to various forms of aesthetics, such as product aesthetics ([ 9]; [11]), graphic aesthetics ([44]), web aesthetics (i.e., aesthetic formality and aesthetic appeal; [70]), and solicitation aesthetics ([68]). In extending the literature, we contrast a novel and aesthetically appealing imaginative display against a non-novel and neutral aesthetic standard display. Retailers use imaginative displays to attract and generate positive customer responses; therefore, we exclude aesthetically unappealing product displays, as merely imagining the unattractive display could negatively affect customers' self-perception and lower their willingness to pay for the product ([24]). Imaginative Display and Purchase BehaviorOur conceptualization of an imaginative display as being novel and aesthetically appealing highlights the combined effects of novelty and aesthetics. In this vein, novelty in the retail context can provide customers with a memorable consumption experience ([58]). A novel product appearance makes the product visually prominent compared with other products, which in turn affects customers' purchase decision ([15]). For instance, [60] show that the novelty of new digital displays in hypermarkets led to a sales lift of 17%, and even when the novelty wore off after five months, the sales lift remained at 3%.Meanwhile, high aesthetic appeal can be pleasurable and has positive effects on customer responses to products and brands ([30]; [63]). For example, the aesthetic appeal of web design can increase online purchases through increased satisfaction ([70]). Similarly, aesthetically appealing packaged goods generate higher purchase intentions and lead to greater market share compared with aesthetically unappealing competitors ([56]). Thus, we hypothesize that an imaginative display heightens visual salience and attractiveness of the product ([15]; [35]), which increases purchase behavior. More formally: H1: An imaginative display, compared with a standard display, increases customers' purchases and purchase intentions. Mediating Effects of Arousal and Inferred Product BenefitsEarly research in psychobiology has established that the collative properties of a stimulus (e.g., novelty, complexity, incongruity) can influence pleasure through the mediating effect of arousal ([ 5]). For example, a work of art that is novel can influence arousal level and, subsequently, pleasure and interest ([ 6]). A recent study of the arrangement of a salad dish on a plate (arranged to look like a painting by Kandinsky vs. a regular dish vs. another organized in a neat but nonartistic way) reveals that the aesthetically pleasing dish enhanced diners' rating of the dish ([45]).Similarly, environmental stimuli can influence individuals' emotional states (i.e., arousal and pleasure dimensions), which in turn influence their behavior ([43]). The arousal dimension reflects customers' feelings related to excitement and stimulation, while the pleasure dimension reflects their positive emotions such as happiness and satisfaction. [20] show that novelty as a measure of information rate is positively related to customer arousal. Other stimuli such as music, scent, and color can also influence arousal ([25]; [47]). For example, familiar music that is played at a novel pitch increases stimulation ([65]). Furthermore, aesthetic formality has a negative influence on arousal, whereas aesthetic appeal has a positive influence on arousal ([70]).Increased arousal in turn positively influences customers' willingness to buy ([ 1]) and product preferences ([18]) due to arousal misattribution ([62]). That is, customers often misattribute the positive feelings from one stimulus to the target product they are evaluating ([18]). Accordingly, we propose that imaginative displays would lead to arousal misattribution, which in turn positively influences customers' purchase of the product. More formally: H2: An imaginative display, compared with a standard display, increases arousal, which in turn increases customers' purchases and purchase intentions.In addition to influencing the affective response of arousal, an imaginative display may also influence customers' cognitive responses simultaneously ([ 7]). For instance, an irregularly sliced graphic design (i.e., novel and aesthetic) in the background of an ad can elicit greater arousal and also impart a more favorable embodied meaning than an intact curved design (i.e., less novel and less aesthetic; [44]). That is, consumers infer meaning and make judgments about the target object when exposed to visual aesthetics ([ 9]). In this vein, in-store display as a form of product design can communicate values, beliefs, and benefits to customers ([ 8]). For example, a novel (i.e., metallic fabric vs. burlap) tablecloth can have a contextual effect on consumers' inferred evaluation of the product placed on it (i.e., trendy vs. natural) ([72]).Moreover, customer perception of an aesthetically appealing ensemble of products can transfer to the evaluation of the individual products ([36]). In particular, in-store displays designed with concept themes can potentially increase perceived value and brand equity of the display products ([42]). For example, a themed in-store display in the form of birds' wings can be used to promote products such as superfoods to evoke their symbolic meanings of lightness and spirituality ([42]). Accordingly, we further propose that a themed imaginative display can communicate embodied meanings that will transfer to the product constituting the display (i.e., inferred product benefits). A favorable inference will lead to greater cognitive elaboration about the product attributes ([40]), which enhances customers' purchase decision. Prior research shows that generating more product attribute–related thoughts in turn enhances product evaluation and purchase decision ([37]). More formally: H3: A themed imaginative display, compared with a standard display, increases the inference of product benefits, which in turn increases customers' purchases and purchase intentions. Moderating Effect of Congruence Between Display Form and Product BenefitThe assumptions underlying H2 and H3 are that arousal elicited by an imaginative display would be misattributed and meaning inferred from the imaginative display would transfer to the product itself, which increase customers' purchases. However, the arousal-based and inference-based effects are context dependent and can have either a positive or negative outcome depending on the context ([11]; [68]; [70]). Thus, we further propose that (in)congruence between display form and perceived product benefit will moderate the effects of the dual processes on purchase intention.Specifically, we draw on research showing that products are evaluated more favorably when they are congruent with similar cues in the environment ([ 4]). For instance, [22] show that congruence between a garment (sleepwear) on display and the appropriate environmental fragrance enhances customers' approach responses, and this effect is mediated by their pleasurable experience. Moreover, conceptual congruence between the thematic display context and the product can improve product evaluation by generating positive feelings and more product attribute-related thoughts ([37]). Thus, congruence between inferred benefits from the display form and perceived product benefit would facilitate arousal misattribution and meaning transfer to increase purchase intention.By contrast, incongruence between inferred benefits from the display form and perceived product benefit would prevent arousal misattribution and dampen meaning transfer to the product, thus lowering purchase intention. For instance, aesthetic enhancement of donation solicitation that is incongruent with cost implications (e.g., using gold ink) can backfire and lower donations ([68]). Moreover, the affective experience of arousal could be positive or negative ([18]; Noseworthy, Di [51]). Incongruity could evoke additional arousal, and extremely high arousal is aversive and leads to negative feelings such as irritation and anxiety, which lower preference for the product ([11]; [51]). To illustrate, an imaginative display in the form of a battle tank could lead customers to feel positive arousal (i.e., pleasant feeling) and infer ""strength and power"" for a product positioned on a congruent benefit (e.g., energy drink), which increases purchase intention. In contrast, although the same battle tank display for a product with an incongruent benefit (e.g., relaxation drink) may also lead customers to feel aroused and infer energy from the display form, they are unrelated to the product itself, which disrupt arousal misattribution and meaning transfer, and subsequently lower purchase intention. Taken together, congruence between display form and perceived product benefit moderates the effects of an imaginative display on purchase behavior. More formally: H4: The effects of an imaginative display (H1) and the underlying processes (H2 and H3) on customers' purchases and purchase intentions are moderated by congruence between display form and perceived product benefit, such that the effects hold when they are congruent but are mitigated when they are incongruent. Study 1: Field Experiment in a Grocery Store Design and ProcedureStudy 1 tests the main effect of an imaginative display on sales revenue in a grocery store in a major Australian city using a one-factor two-level (product display: imaginative vs. standard) between-subjects design. The imaginative display was designed in the form of a 17-story quasi-circular cone on a cuboid base constructed using boxes of facial tissues (retail price $1.99), while the standard display consisted of only the cuboid base (Appendix A). A pretest (between-subjects design, N = 115) on the display showed that the imaginative display was perceived to be more novel (Mimaginative = 5.70, SD =.98 vs. Mstandard = 2.30, SD = 1.57, F( 1, 133) = 195.18, p <.001, ηp2 =.63) and aesthetically appealing (Mimaginative = 5.95, SD =.83 vs. Mstandard = 3.72, SD = 1.18, F( 1, 133) = 136.53, p <.001, ηp2 =.547) than the standard display.[ 7]The display was located near the checkout counter. Store employees restocked the facial tissues after each purchase. Customers were not aware of the research being conducted. The store manager provided information on the daily facial tissue unit sales, the store's daily revenue, and the relevant cost information. We conducted this study over a two-week period (Week 1 = standard display, Week 2 = imaginative display). Results and Discussion Sales revenueWe first calculated the daily facial tissue sales revenue (M = 24.31, SD = 12.52) by multiplying the daily quantity sold (M = 12.21, SD = 6.29) with the unit price ($1.99). A simple regression revealed a significant effect of product display (1 = imaginative, 0 = standard) on daily facial tissue sales revenue (β =.58, t(12) = 2.45, p =.031), which supports H1.In addition, we conducted a stepwise regression analysis on daily facial tissue sales revenue to capture the unique variance explained by the imaginative display by considering the potential covarying effect of daily store revenue. We first entered daily store revenue in the baseline model, and the overall model was nonsignificant (R2 =.15, F( 1, 12) = 2.11, p =.17). Thus, daily facial tissue sales revenue did not covary with daily store revenue (β =.38, t(12) = 1.45, p =.17). Next, we added product display into the model, and the overall model became significant (R2 =.56, F( 2, 11) = 6.91, p =.011). We observed a significant effect of the imaginative display on daily facial tissue sales revenue (β =.64, t(11) = 3.18, p =.009), in addition to a significant effect of daily store revenue (β =.48, t(11) = 2.35, p =.038). Thus, the explanatory power of the model was improved due to the effect of the imaginative display (ΔR2 =.41, F-change ( 1, 11) = 10.09, p =.009). Return on investmentWe also calculated return on investment (ROI) of the imaginative display using cost and sales information from the retailer. The unit sales lift, or difference in aggregate daily facial tissue unit sales between Week 2 (imaginative display) and Week 1 (standard display), was 49 units. The net profit per box of tissue was $.89 (i.e., total net profit = $43.61), while the extra labor cost to set up and maintain the imaginative display relative to the standard display was 1.5 hours, equivalent to $28.50. Thus, ROI for the imaginative versus standard display was (43.61 – 28.50) / 28.50 = 53.02%. DiscussionStudy 1 provides field evidence supporting the positive effect of an imaginative display on sales revenue and ROI (H1). However, we acknowledge that the imaginative display in this study was taller than the standard display; thus, it is plausible that the effect could be due to the height difference. To eliminate this potential confounding effect, we conducted Study 2. Study 2: Field Experiment in a Confectionery StoreWe designed Study 2 to replicate the main effect of an imaginative display on sales in a confectionery store in a large Australian city. To minimize biases from customers' preexisting brand preferences, we chose a little-known chocolate brand, Duc d'O, as the product stimulus. Design and ProcedureDifferent from Study 1, Study 2 uses a one-factor three-level (product display: imaginative vs. standard–high vs. standard–low) between-subjects design. All displays were constructed using boxes of Duc d'O chocolates (Appendix A). The imaginative display was designed in the form of a quasi-cylindrical form on a cuboid base of chocolates. The standard–high display was a cuboid-shaped display on the same base, while the standard–low display had only an elevated cuboid base. The imaginative display was the same height as the standard–high display. We placed the product display near the entrance/exit of the store. This study took place over four days. We used a different display on each of the first three days and rotated the order of the three displays on the last day. Next to the display were two signs showing the price ($5) and inviting customers to take part in the survey in exchange for a $10 store voucher.A research assistant stood near the entrance and invited each approaching customer to participate in the survey. Customers who agreed to take part received a paper questionnaire on a clipboard. Participants were first asked some questions about their store perceptions. Then they were asked to look at the product display and evaluate it in terms of novelty (M = 4.19, SD = 1.78, r =.91) and aesthetics (M = 4.99, SD = 1.33, α =.96). Participants also rated the extent to which they liked eating chocolates (M = 6.16, SD = 1.02, α =.93) and their familiarity with the Duc d'O brand (M = 2.05, SD = 1.57, α =.95). All measures were rated on seven-point scales (see Appendix B). Finally, participants indicated their gender and age.After completing the questionnaire, participants were thanked and given a $10 store voucher that was valid only on that day and could be used toward any purchase, with no minimum spending. When they finished shopping, they redeemed the voucher at the counter. The cashier retained the voucher, stapled a duplicate receipt to it, and recorded the number of boxes of Duc d'O chocolates purchased using the voucher at the end of each session. Results and Discussion Participation rateAcross the four-day experiment, 1,416 customers purchased products in the store (386, 356, 302, and 372 for each day, respectively), among whom 250 customers (66.80% female, Mage = 39.79 years; N = 84, 83, and 83 for the imaginative, standard–high, and standard–low displays, respectively) participated in the study (17.66% participation rate). Manipulation checksAnalysis of variance (ANOVA) results show that the three product displays differed significantly in novelty (F( 2, 247) = 35.06, p <.001, ηp2 =.22). Participants perceived the imaginative display to be more novel (M = 5.35, SD = 1.30) than the standard–high (M = 3.77, SD = 1.73, t(247) = 7.84, p <.001) and standard–low (M = 3.43, SD = 1.68, t(247) = 6.46, p <.001) displays, with no significant difference between the standard displays (p =.171). Similarly, ANOVA results showed that the three product displays differed significantly in aesthetics (F( 2, 247) = 26.85, p <.001, ηp2 =.18). The imaginative display was perceived to be more aesthetically appealing (M = 5.76, SD =.89) than the standard–high (M = 4.72, SD = 1.28, t(247) = 6.91, p <.001) and standard–low (M = 4.47, SD = 1.39, t(247) = 5.55, p <.001) displays, with no significant difference between the standard displays (t(247) = 1.36, p =.175). Thus, the three product displays were manipulated successfully. Moreover, participants across the three display conditions did not differ in their liking for chocolates (F( 2, 247) =.61, p =.55, ηp2 =.005) or brand familiarity (F( 2, 247) =.95, p =.39, ηp2 =.008). Actual purchaseWe first coded purchases of Duc d'O chocolates using the voucher (1 = purchase, 0 = no purchase). A binary logistic regression revealed that customers in the imaginative display condition (48.81%) purchased significantly more chocolates than those in the standard–high (16.87%; B = −1.55, χ2( 1) = 17.93, p <.001) and standard–low (19.28%; B = −1.38, χ2( 1) = 15.33, p <.001) display conditions, with no significant difference between the standard display conditions (B =.16, χ2( 1) =.16, p =.69). Including liking for chocolates and brand familiarity as covariates did not change the conclusion. Liking for chocolates and brand familiarity had no significant effects on actual purchase (ps >.11). These results support H1. DiscussionStudy 2 further supports the positive effect of the imaginative display on actual purchase relative to the standard displays, regardless of height. While Studies 1 and 2 provide external validity for the effect of the imaginative display (H1), the field settings precluded a controlled environment to test the proposed underlying mechanisms, for which we turned to laboratory experiments. Study 3a: Mediating Role of Arousal (Measured)Studies 3a and 3b test the mediating role of arousal underlying the effect of an imaginative display (H2) using two complementary designs: Study 3a uses a measured-mediation design while Study 3b uses an experimental causal-chain design ([66]). Moreover, as visual salience of a novel display could draw customer attention, which would increase their purchase intention ([35]), and visual complexity and perceived difficulty in constructing the novel display could also heighten visual salience ([21]), we measured attention drawing, visual complexity, and perceived difficulty to elicit the unique effect of arousal in Study 3a. Design, Procedure, and MeasuresWhile Study 2 eliminated display height as an alternative explanation, the quantity of items in the product display could also potentially influence customer perceptions and their purchase decision. Therefore, to eliminate this alternative explanation, Study 3a uses a one-factor three-level (product display: imaginative vs. standard–large quantity vs. standard–small quantity) between-subjects design. We recruited 261 participants on Amazon Mechanical Turk (MTurk) who received financial compensation. We excluded six participants who failed the attention check questions, leaving 255 responses for the analyses (47.84% female; Mage = 37.11 years, SD = 12.28).The imaginative display was in the form of a quasi-spiral-staircase structure above a cuboid base constructed using boxed tubes of toothpaste. The standard–small and standard–large quantity displays had only the cuboid base, with more items in the standard–large display (see Appendix A). The imaginative and standard–large displays had the same number of items.Participants were randomly assigned to one of the three product display conditions. They read a scenario about a trip to the grocery store to buy toothpaste in which they encountered a product display of a new, unspecified brand of toothpaste. They saw an image of the product display and indicated their purchase intention for the toothpaste (M = 4.13, SD = 1.61, α =.94) and level of arousal (M = 4.62, SD = 1.41, α =.92). They also rated the product display in terms of attention drawing (M = 5.86, SD = 1.21, α =.90), visual complexity (M = 3.52, SD = 1.99, r =.98), power (1 = ""not at all powerful,"" and 7 = ""extremely powerful,"" M = 3.97, SD = 1.44), and perceived difficulty in construction (M = 4.70, SD = 1.76, α =.94). Furthermore, they evaluated novelty (M = 4.05, SD = 2.05, r =.92), aesthetics (M = 4.87, SD = 1.43, α =.97), and perceived quantity (1 = ""very scarce,"" and 7 = ""very abundant,"" M = 6.47, SD =.87) of the product display. Unless otherwise specified, we used the same measurement items across all studies (Appendix B). Results and Discussion Manipulation checksANOVA results showed that the three product displays differed significantly in novelty (F( 2, 252) = 97.12. p <.001, ηp2 =.44). The imaginative display was perceived to be more novel (M = 5.96, SD = 1.09) than the standard–large quantity (M = 3.27, SD = 1.79, t(252) = 11.34, p <.001) and standard–small quantity (M = 2.95, SD = 1.65, t(252) = 12.71, p <.001) displays, with no significant difference between the standard displays (t(252) = 1.34, p =.18). Similarly, ANOVA results showed that the three product displays differed significantly in aesthetics (F( 2, 252) = 30.36, p <.001, ηp2 =.19). The imaginative display was perceived to be more aesthetically appealing (M = 5.76, SD = 1.17) than the standard–large quantity (M = 4.40, SD = 1.23, t(252) = 6.86, p <.001) and standard–small quantity (M = 4.45, SD = 1.45, t(252) = 6.65, p <.001) displays, with no significant difference between the standard displays (t(252) =.23, p =.82). Thus, we conclude that the three product displays were manipulated successfully.ANOVA results revealed that product display manipulation did not significantly affect perceived quantity (F( 2, 252) = 1.71, p =.18, ηp2 =.013). In addition, we observed a significant main effect of product display on power (F( 2, 252) = 11.84, p <.001, ηp2 =.086). Participants perceived the imaginative display (M = 4.56, SD = 1.32) to embody more power than the standard–large (M = 3.80, SD = 1.34, t(252) = 3.57, p <.001) and standard–small (M = 3.57, SD = 1.49, t(252) = 4.66, p <.001) displays, with no significant difference between the standard displays (t(252) = 1.09, p =.278). Thus, we controlled for power in the subsequent analyses. Purchase intentionAnalysis of covariance (ANCOVA) results revealed a significant main effect of product display on purchase intention (F( 2, 251) = 3.97, p =.02, ηp2 =.031). Planned contrasts showed that the imaginative display led to higher purchase intention (M = 4.79, SD = 1.53) compared with the standard–large quantity (M = 3.93, SD = 1.46; t(252) = 3.61, p <.001) and standard–small quantity (M = 3.69, SD = 1.66; t(252) = 4.59, p <.001) displays, with no significant difference between the standard displays (t(252) =.98, p =.33). These results further support H1. ANOVA results without power as a covariate did not change the significant effect of product display (F( 2, 252) = 11.64, p <.001, ηp2 =.084). ArousalANCOVA results revealed a significant main effect of product display on arousal (F( 2, 251) = 18.00, p <.001, ηp2 =.125). Specifically, the imaginative display (M = 5.43, SD = 1.17) led to greater feelings of arousal than the standard–large quantity (M = 4.07, SD = 1.20, t(252) = 6.88, p <.001) and standard–small quantity (M = 4.36, SD = 1.47, t(252) = 5.46, p <.001) displays, with no significant difference between the standard displays (t(252) = −1.39, p =.15). ANOVA results without power as the covariate did not change the significant effect of display on arousal (F( 2, 252) = 26.33, p <.001, ηp2 =.173).Similarly, ANCOVA results showed the significant main effects of product display on attention drawing (F( 2, 251) = 7.71, p <.01, ηp2 =.058), visual complexity (F( 2, 251) = 66.76, p <.001, ηp2 =.347), and perceived difficulty (F( 2, 251) = 51.98, p <.001, ηp2 =.293). Controlling for these variables and power, ANCOVA results did not change the significant main effects of product display on purchase intention (F( 2, 248) = 3.88, p =.022, ηp2 =.03) and arousal (F( 2, 248) = 7.02, p <.001, ηp2 =.054). Mediation analysesNext, we conducted multicategorical mediation analyses using [28] PROCESS macro (Model 4). We constructed the bootstrapping at a 95% confidence interval (CI) with 10,000 samples with the imaginative display as the reference group, such that we had two dummy variables: D1, which compared the imaginative display with the standard–small display (imaginative display = 0, standard–small display = 1, standard–large display = 0), and D2, which compared the imaginative display with the standard–large display (imaginative display = 0, standard–small display = 0, standard–large display = 1). The model included display as the independent variable, arousal as the focal mediator, and attention drawing, visual complexity, and perceived difficulty as parallel mediators. In so doing, we sought to elicit the unique effect of arousal above and beyond these other potential effects.Results showed only the mediation effects of arousal for the imaginative display compared with the standard–small display (D1: b = –.18, SE =.08, 95% CI = [–.355, –.050]), and for the comparison between the imaginative display and the standard–large display (D2: b = –.26, SE =.09, 95% CI = [–.468, –.085]). We observed no direct mediation effects of attention drawing (D1: b = −.005, SE =.05, 95% CI = [–.114,.101]; D2: b = –.004, SE =.04, 95% CI = [−.194,.081]), visual complexity (D1: b = −.006, SE =.18, 95% CI = [–.391,.353]; D2: b = −.006, SE =.17, 95% CI = [−.357,.323]), and perceived difficulty (D1: b =.28, SE =.16, 95% CI = [−.052,.589]; D2: b =.21, SE =.13, 95% CI = [–.042,.450]).Moreover, we tested attention drawing and arousal as serial mediators between display and purchase intention using [28] PROCESS macro (Model 6; constructed at 95% CI with 10,000 bootstrapped samples). Results showed the significant single mediation of arousal (path 1) for the imaginative display versus standard–small display comparison (D1: b = –.31, SE =.11, 95% CI = [–.552, –.125]), and for the imaginative display versus standard–large display comparison (D2: b = –.42, SE =.12, 95% CI = [–.678, –.201]). Moreover, there were significant serial mediation effects (path 2) for D1 (b = –.06, SE =.03, 95% CI = [–.127, –.018]) and D2 (b = –.05, SE =.02, 95% CI = [–.097, –.014]). However, further analyses contrasting the two indirect effects (path 1 minus path 2) revealed that the single mediation effects of arousal were significantly stronger than the serial mediation effects in both comparisons (contrast for D1: b = –.25, SE =.11, 95% CI = [–.480, –.061]; contrast for D2: b = –.37, SE =.12, 95% CI = [−.633, −.163]). These results suggested that attention drawing could influence arousal, consistent with the literature ([ 6]). However, the effect of attention drawing on purchase behavior was fully mediated by arousal. That is, arousal alone was sufficient to explain the proposed effect after accounting for the effect of attention drawing. DiscussionStudy 3a supports the mediation effect of arousal underlying the effect of the imaginative display on purchase intention, above and beyond the effects of visual complexity and perceived difficulty in constructing the display. Study 3b: Mediating Role of Arousal (Manipulated)We conducted Study 3b to replicate the mediating effect of measured arousal found in Study 3a using a causal-chain design ([66]). If arousal is indeed the underlying mechanism, it should generate an effect similar to that of the imaginative display on purchase intention. Thus, if arousal was induced prior to showing participants the product displays, the relative advantage of the imaginative display on purchase intention against the standard display would diminish. Furthermore, we conjectured that for the imaginative display to be effective, it should be perceived to be both novel and aesthetically appealing. Thus, if one element (e.g., aesthetic appeal) was missing, then the efficacy of the imaginative display would be diminished. To test this assumption, Study 3b incorporates a new display condition that was more novel but not different in aesthetics compared with the standard display. We expected that the effect of this novel but nonaesthetic display would not differ from that of the standard display. Design, Procedure, and MeasuresStudy 3b uses a 3 (product display: imaginative vs. novel–nonaesthetic vs. standard) × 2 (arousal: high vs. low) between-subjects design. We recruited 279 participants (43.37% female; Mage = 36.87 years, SD = 10.73) on MTurk, who received financial compensation.We first primed arousal following Noseworthy, Di Muro, and Murray's procedure ([51]; Study 1). Participants were randomly assigned to one of two groups of images, all drawn from the International Affective Picture System ([38]). Each group contained 18 images, and each image was displayed for six seconds. Images in the two groups were similar in pleasantness but varied in arousal. Participants indicated progressive change in their arousal level on an adjustable semantic differential scale (−50 = very relaxed, +50 = very excited). When they felt no further change in their arousal level, they clicked on the ""No change"" button and transitioned to a shopping scenario for toothpaste. Participants saw one of the three displays. The imaginative display had a quasi-spiral-staircase form as in Study 3a while the novel–nonaesthetic display had a pillar form with the same height as the imaginative display. The standard display was the standard-large quantity display used in Study 3a. All three displays had the same number of items in them (Appendix A). In addition, we removed the background of all images for a cleaner test. Following that, participants indicated their purchase intention for the toothpaste (M = 4.19, SD = 1.38, α =.93). Results and Discussion Pretest of display stimuliWe conducted a pretest to verify the manipulations of the three displays (between-subjects design, N = 96). ANOVA results showed that the three displays differed significantly in novelty (F( 2, 93) = 15.85, p <.001, ηp2 =.254). The imaginative display was perceived to be more novel (M = 5.97, SD =.80) than the novel–nonaesthetic (M = 4.65, SD = 1.62, t(93) = 3.40, p =.001) and standard (M = 3.84, SD = 1.91, t(93) = 5.96, p <.001) displays, and the novel–nonaesthetic display was more novel than the standard display (t(93) = 2.12, p =.037). Similarly, ANOVA results showed that the three displays differed significantly in aesthetics (F( 2, 93) = 5.61, p =.005, ηp2 =.108). The imaginative display was perceived to be more aesthetically appealing (M = 5.78, SD =.94) than the novel–nonaesthetic (M = 5.01, SD = 1.40, t(93) = 2.32, p =.022) and standard (M = 4.72, SD = 1.51, t(93) = 3.26, p =.002) displays, with no significant difference between the novel–nonaesthetic and standard displays (t(93) =.89, p =.38). Thus, the three product displays were manipulated successfully. Manipulation checkANOVA results showed that participants reported being more excited in the arousal condition (M = 18.80, SD = 23.42) and more relaxed in the nonarousal condition (M = −15.93, SD = 21.56; F( 1, 277) = 165.97, p <.001, ηp2 =.375). Thus, our manipulation of arousal was successful. Purchase intentionA 3 (display) × 2 (arousal) ANOVA on purchase intention revealed the significant main effects of product display (F( 2, 273) = 4.48, p =.012, ηp2 =.032) and arousal (F( 1, 273) = 8.38, p =.004, ηp2 =.030). Importantly, we observed a significant interaction effect of product display × arousal (F( 2, 273) = 3.09, p =.047, ηp2 =.022). Decomposing the interaction, the simple effect of display was significant in the low-arousal condition (F( 2, 273) = 7.45, p =.001, ηp2 =.052) but not in the high-arousal condition (F( 2, 273) =.08, p =.924, ηp2 =.001). Specifically, in the low-arousal condition, the imaginative display led to significantly higher purchase intention (M = 4.57, SD = 1.21) compared with the novel–nonaesthetic (M = 3.63, SD = 1.20, t(136) = 3.69, p <.001) and standard (M = 3.66, SD = 1.31, t(136) = 3.49, p =.001) displays, with no difference between the novel–nonaesthetic and standard displays (t(136) = −.10, p =.92). However, in the high-arousal condition, purchase intention did not significantly differ across the three displays (Mimaginative = 4.48, SD = 1.33 vs. Mnovel–nonaesthetic = 4.41, SD = 1.38 vs. Mstandard = 4.38, SD = 1.53, all ps >.70). These results support H2.Viewed another way, priming high arousal increased purchase intention for the novel–nonaesthetic (Mlow-arousal = 3.63 vs. Mhigh-arousal = 4.41, F( 1, 273) = 7.87, p =.005, ηp2 =.028) and standard (Mlow-arousal = 3.66 vs. Mhigh-arousal = 4.38, F( 1, 273) = 6.55, p =.011, ηp2 =.023) displays, but not for the imaginative display (F( 1, 273) =.124, p =.725, ηp2 =.00). DiscussionStudies 3a and 3b affirm the positive effect of the imaginative displays on customers' purchase intentions (H1), as mediated by arousal (H2). While Study 3a measured arousal to establish causality between display form and arousal, Study 3b manipulated arousal to establish causality between arousal and purchase intention. Importantly, Study 3b results support our conjecture about the two critical elements of novelty and aesthetic appeal embedded in the imaginative display driving arousal. Specifically, the imaginative display led to higher purchase intention compared with the novel–nonaesthetic and standard displays, with no difference between the latter two displays. Moreover, inducing arousal enhanced purchase intentions for the novel–nonaesthetic and standard displays, similar to the effect of the imaginative display. Study 4: The Dual Mediation of Arousal and Inferred Product BenefitsHaving shown the affect-based arousal process underlying the effects of imaginative displays in Studies 3a and 3b (H2), we next turn to examining the cognition-based inference process (i.e., inferred product benefits; H3). We proposed that customers would infer certain meanings from themed imaginative displays, which would transfer to the product constituting the display, thus influencing customers' purchase decision. We tested the dual mechanisms of arousal (H2) and inferred product benefits (H3) simultaneously in Study 4.While Studies 3a and 3b used unbranded products to increase generalizability and practical implications of the hypothesized effects (H1–H3), Study 4 used two actual brands, Charmin and Sorbent. A pretest (within-subject design, N = 102) showed that U.S. participants were significantly more familiar with the Charmin brand (M = 6.36, SD = 1.17) than with the Sorbent brand (M = 1.49, SD = 1.13, t(101) = 27.17, p <.001). We expected that the effect of the imaginative display on purchase intention would apply to both familiar and less familiar brands. Design, Procedure, and MeasuresStudy 4 used a 2 (product display: imaginative vs. standard) × 2 (brand: Charmin vs. Sorbent) between-subjects design. We recruited 256 participants on MTurk (44.92% female, Mage = 37.30 years, SD = 9.99), who received financial compensation.The imaginative display was in the form of a bear stacked above a cuboid base constructed using individually wrapped rolls of bathroom tissue, while the standard display consisted of the elevated cuboid base of the display. Both displays had the same number of items in them (Appendix A). Participants read a scenario about a recent trip to the grocery store to buy bathroom tissue and were randomly assigned to see one of the two product displays with either Charmin or Sorbent brand. Following that, participants indicated their purchase intention (M = 4.41, SD = 1.66, α =.95), feeling of arousal (M = 4.04, SD = 1.69, α =.93), and inferred strength of the bathroom tissue (1 = ""not at all strong,"" and 7 = ""very strong;"" M = 4.93, SD = 1.47). Results and Discussion Pretest of display stimuliWe conducted a pretest to verify the manipulations of the two product displays (between-subjects design, N = 101). ANOVA results showed that the imaginative display was perceived to be more novel (M = 6.35, SD =.73) than the standard display (M = 2.81, SD = 1.94, F( 1, 99) = 145.31, p <.001, ηp2 =.595). Similarly, the imaginative display was perceived to be more aesthetically appealing (M = 5.88, SD = 1.05) than the standard display (M = 4.39, SD = 1.30, F( 1, 99) = 39.66, p <.001, ηp2 =.286). Thus, the two product displays were manipulated successfully. Purchase intentionA 2 (display) × 2 (brand) ANOVA showed a significant main effect of display (F( 1, 252) = 10.24, p =.002, ηp2 =.039), such that the imaginative display led to higher purchase intention (M = 4.73, SD = 1.58) than the standard display (vs. M = 4.09, SD = 1.68). We also observed a significant main effect of brand (F( 1, 252) = 20.08, p <.001, ηp2 =.074), such that participants had higher purchase intention for the more familiar Charmin brand (M = 4.87, SD = 1.59) than for the less familiar Sorbent brand (M = 3.98, SD = 1.62). However, the interaction effect of display × brand was nonsignificant (F( 1, 252) =.08, p =.778, ηp2 =.00).Planned contrasts showed that the imaginative display increased purchase intention compared with the standard display for both Charmin (Mimaginative = 4.33, SD = 1.60 vs. Mstandard = 3.64, SD = 1.57; F( 1, 252) = 4.13, p =.043, ηp2 =.016) and Sorbent brands (Mimaginative = 5.16, SD = 1.45 vs. Mstandard = 4.58, SD = 1.67; F( 1, 252) = 6.26, p =.013, ηp2 =.024). Thus, the imaginative display increased purchase intention, regardless of brand familiarity. ArousalSimilarly, a 2 × 2 ANOVA revealed a significant main effect of product display (F( 1, 252) = 83.56, p <.001, ηp2 =.249), such that the imaginative display led to greater arousal (M = 4.89, SD = 1.56) than the standard display (M = 3.21, SD = 1.38). However, the interaction effect of display × brand was nonsignificant (F( 1, 252) =.83, p =.364, ηp2 =.003). Planned contrasts showed that the imaginative display led to higher arousal than the standard display for both Charmin (Mimaginative = 5.00, SD = 1.51 vs. Mstandard = 3.49, SD = 1.49; F( 1, 252) = 32.86, p <.001, ηp2 =.115) and Sorbent (Mimaginative = 4.78, SD = 1.63 vs. Mstandard = 2.95, SD = 1.22; F( 1, 252) = 52.12, p <.001, ηp2 =.171). Thus, the imaginative display led to greater arousal, regardless of brand familiarity. Inferred product benefitA 2 × 2 ANOVA on inferred strength of the bathroom tissue showed a significant main effect of display (F( 1, 252) = 35.12, p <.001, ηp2 =.122), such that the bathroom tissue was perceived to be stronger for the imaginative display (M = 5.44, SD = 1.36) than for the standard display (M = 4.42, SD = 1.41). We observed a marginally significant interaction effect of display × brand (F( 1, 252) = 3.75, p =.053, ηp2 =.015). Planned contrasts showed that participants inferred greater strength for the bathroom tissue for the imaginative display than for the standard display for both Charmin (Mimaginative = 5.50, SD = 1.38 vs. Mstandard = 4.79, SD = 1.47; F( 1, 252) = 8.41, p =.004, ηp2 =.032) and Sorbent (Mimaginative = 5.38, SD = 1.34 vs. Mstandard = 4.07, SD = 1.26; F( 1, 252) = 30.50, p <.001, ηp2 =.108) brands. Thus, the imaginative display led to a meaning transfer of inferred product benefit (i.e., strength) from the imaginative display to the displayed product, regardless of brand familiarity. Mediation analysisWe tested for dual mediation using PROCESS Model 4 with arousal and inferred product benefit as parallel mediators. Results showed significant dual mediation effects of arousal (b =.73, SE =.13, 95% CI = [.456, 1.028]) and inferred product benefit (b =.36, SE =.09, 95% CI = [.185,.565]) between product display and purchase intention. Including the brand as a covariate did not change the dual mediation effects of arousal (b =.70, SE =.14, 95% CI = [.453,.985]) and inferred product benefit (b =.34, SE =.09, 95% CI = [.173,.531]). These results support H2 and H3. DiscussionStudy 4 supports the dual mediation effects of arousal and inferred product benefits underlying the effects of the imaginative display for two actual brands, one familiar and the other less familiar. That is, affectively, consumers felt greater arousal; cognitively, consumers inferred greater product strength from the themed imaginative display in the form of a bear; and both mechanisms increased their purchase intention. Study 5: Moderating Role of Congruence Between Display Form and Product BenefitWe designed Study 5 to examine the moderating effect of congruence between display form and perceived product benefit on the core effects (H4). We hypothesized that the core effects are moderated by congruence between display form and perceived product benefit, such that congruence would enhance the effects of the imaginative display on customers' purchase intention, while incongruence would attenuate or even reverse the effects. Design and ProcedureStudy 5 used a 2 (product display: imaginative vs. standard) × 3 (product benefit: energized vs. relaxed vs. control) between-subjects design. We recruited 480 participants on Prolific (65.00% female, Mage = 33.65 years, SD = 8.06) in exchange for financial compensation.Participants read a scenario about having a very busy period at work and going to the grocery store to buy ( 1) energy drinks to boost attention span and energy levels (energized condition), ( 2) relaxation drinks to reduce stress and calm down (relaxed condition), or ( 3) natural mineral water (control condition). In the store, they came across either an imaginative display or a standard display of a new beverage. The imaginative display was in the form of a battle tank stacked above a cuboid base constructed using cans of a beverage, while the standard display consisted of the elevated cuboid base. Both displays had the same number of items in them (Appendix A). Following that, participants indicated their purchase intention for the beverage (M = 3.32, SD = 1.64, α =.94), feeling of arousal from the display (M = 3.89, SD = 1.46, α =.90), inference of energy from the display (M = 3.86, SD = 1.37, α =.91), and inference of relaxation from the display (M = 2.93, SD = 1.40, α =.96) (Appendix B). Results and Discussion Pretest of stimuli displayWe conducted a pretest to verify the manipulations of the two display stimuli (between-subjects design, N = 99). ANOVA results showed that the imaginative display was perceived to be more novel (M = 6.12, SD = 1.24) compared with the standard display (M = 3.16, SD = 1.93; F( 1, 97) = 82.30; p <.001, ηp2 =.46). Similarly, the imaginative display was perceived to be more aesthetically appealing (M = 5.45, SD = 1.50) than the standard display (M = 4.74, SD = 1.17; F( 1, 97) = 6.74, p =.011; ηp2 =.065).Moreover, participants inferred the imaginative display to have greater energy benefit (Mimaginative = 5.61, SD = 1.35 vs. Mstandard = 4.01, SD = 1.74; F( 1, 97) = 26.01, p <.001, ηp2 =.212), but lower relaxation benefit compared with the standard display (Mimaginative = 2.39, SD = 1.63 vs. Mstandard = 3.23, SD = 1.62; F( 1, 97) = 6.52, p =.012, ηp2 =.063). Thus, the product displays and inferred benefits were manipulated successfully. Pretest of perceived product benefitsWe also conducted a pretest to verify the perceived product benefits (between-subjects design, N = 111). Participants were asked to think about an energy drink (vs. relaxation drink) and indicate the extent to which the drink would make them energized (1 = ""not at all energized,"" and 7 = ""extremely energized;"" M = 4.24, SD = 1.67) and relaxed (1 = ""not at all relaxed,"" and 7 = ""extremely relaxed;"" M = 4.52, SD = 1.99) after consuming the beverage.ANOVA results showed that the energy drink led participants to perceive feeling more energized (M = 4.98, SD = 1.16) compared with the relaxation drink (M = 3.43, SD = 1.78; F( 1, 109) = 29.92, p <.001, ηp2 =.215). Conversely, the relaxation drink led participants to perceive feeling more relaxed (M = 5.81, SD = 1.27) than they did with the energy drink (M = 3.34, SD = 1.80; F( 1, 109) = 68.21, p <.001, ηp2 =.385). Thus, product benefits were manipulated successfully. Purchase intentionA 2 × 3 ANOVA on purchase intention revealed a significant interaction effect of display × product benefit (F( 2, 474) = 16.44, p <.001, ηp2 =.065). We observed a main effect of product benefit (F( 2, 474) = 19.44, p <.001, ηp2 =.076). The effect of product display was nonsignificant (F( 1, 474) =.53, p =.47, ηp2 =.001). Decomposing the interaction (Figure 2), planned contrasts showed that the imaginative display increased purchase intention for the energy drink compared with the standard display (Mimaginative = 4.33, SD = 1.75 vs. Mstandard = 3.39, SD = 1.58; F( 1, 474) = 14.38, p <.001, ηp2 =.029). In contrast, the imaginative display lowered purchase intention for the relaxation drink compared with the standard display (Mimaginative = 2.26, SD = 1.29 vs. Mstandard = 3.31, SD = 1.60; F( 1, 474) = 18.48, p <.001, ηp2 =.038). The effect of display was nonsignificant for the natural mineral water (p =.43).Graph: Figure 2. Interaction effects of display form and perceived product benefit (Study 5).Notes: Error bars = ±1 SE. ***p <.001.Viewed another way, relative to the control condition (i.e., mineral water), the imaginative display (F( 2, 474) = 35.80, p <.001, ηp2 =.131) increased purchase intention for the energy drink (Menergy = 4.33 vs. Mmineral = 3.26, t(237) = 4.22, p <.001) but lowered purchase intention for the relaxation drink (Mrelaxation = 2.26 vs. Mmineral = 3.26, t(237) = −4.44, p <.001). For the standard display, participants' purchase intentions did not significantly differ across the three product conditions (all ps >.56). Thus, compared with the standard display, the imaginative display in the form of a battle tank increased purchase intention when it was congruent with the product benefit (i.e., energy) but decreased purchase intention when it was incongruent with the product benefit (i.e., relaxation), in support of H4. ArousalA 2 × 3 ANOVA on arousal revealed a significant effect of product display (F( 1, 474) = 107.99, p <.001, ηp2 =.186), such that the imaginative display evoked greater arousal (M = 4.51, SD = 1.32) compared with the standard display (M = 3.26, SD = 1.30). We observed a significant effect of product benefit (F( 2, 474) = 4.44, p =.012, ηp2 =.018) but a nonsignificant interaction of display × product benefit (F( 2, 474) =.68, p =.51, ηp2 =.003). Inferred benefit of energyA 2 × 3 ANOVA on inferred energy revealed a significant effect of product display (F( 1, 474) = 18.71, p <.001, ηp2 =.038), such that participants inferred greater energy benefit from the imaginative display (M = 4.12, SD = 1.34) than from the standard display (M = 3.59, SD = 1.35). We observed a significant effect of product benefit (F( 2, 474) = 11.04, p <.001, ηp2 =.045) but a nonsignificant interaction effect of display × product benefit (F( 2, 474) =.33, p =.72, ηp2 =.001). Moderated mediation analysisTo test the moderating effects of congruence between the dual mechanisms and purchase intention (H4), we conducted a moderated mediation analysis using PROCESS Model 15 ([28]). We specified product benefit (i.e., the moderator) as a multicategorical moderator with the mineral water as the reference group, which resulted in two dummy variables: D1 compared the relaxation drink with the control condition (mineral water = 0, relaxation drink = 1, energy drink = 0), while D2 compared the energy drink with the control condition (mineral water = 0, relaxation drink = 0, energy drink = 1). Moreover, we included inferred relaxation benefit as an alternative explanation for the reversed effect of the display on the lower purchase intention for the relaxation drink.As expected, the moderating effects of perceived product benefit on purchase intention were qualified by the three significant interaction effects of display × product benefit (F( 2, 465) = 4.79, p =.009), arousal × product benefit (F( 2, 465) = 3.44, p =.033), and inferred energy × product benefit (F( 2, 465) = 3.08, p =.046) but a nonsignificant interaction of inferred relaxation × product benefit (F( 2, 465) =.87, p =.419). Specifically, we observed a significant interaction of display × D2 (b =.69, SE =.31, t = 2.18, p =.03) but a nonsignificant interaction of display × D1 (b = –.28, SE =.32, t = –.87, p =.38) on purchase intention, suggesting that product benefit moderated the effect of display form on purchase intention. Moreover, we observed a significant interaction of inferred energy × D2 (b =.33, SE =.13, t = 2.46, p =.014) but a nonsignificant interaction of inferred energy × D1 (b =.19, SE =.11, t = 1.62, p =.11) on purchase intention, suggesting that product benefit moderated the effect of inferred energy from the display on purchase intention. Conversely, we observed a significant interaction of arousal × D1 (b = −.29, SE =.11, t = −2.47, p =.014) but a nonsignificant interaction of arousal × D2 (b = −.08, SE =.12, t = −.63, p =.52) on purchase intention, suggesting that product benefit moderated the effect of arousal from the display on purchase intention.Importantly, bootstrapping results showed a significant moderated mediation effect via inference of energy for the energy drink (D2: index =.17, SE =.09, 95% CI = [.018,.366]) but not for the relaxation drink (D1: index =.10, SE =.08, 95% CI = [–.038,.271]). Conversely, the data showed a significant moderated mediation effect via arousal for the relaxation drink (D1: index = −.36, SE =.17, 95% CI = [−.701, −.050]) but not for the energy drink (D2: index = −.10, SE =.18, 95% CI = [−.465,.260]),[ 8] suggesting an aversive effect of arousal for the incongruent product. We observed nonsignificant moderated mediation effects through inference of relaxation for both D1 (95% CI = [−.197,.113]) and D2 (95% CI = [–.116,.253]). These results supported the second-stage moderated mediation effect as proposed (H4). DiscussionStudy 5 supports the moderating effects of congruence between display form and perceived product benefit on the main effect of the imaginative display on purchase intention, and on the effects of arousal and inferred product benefits on purchase intention (H4). Compared with a standard display, the themed imaginative display (i.e., battle tank) increased purchase intention for a congruent product (i.e., energy drink) due to the positive effect of arousal and inferred product benefit. Conversely, the same imaginative display lowered purchase intention for an incongruent product (i.e., relaxation drink) due to the aversive effect of arousal. General DiscussionThe present research extends the literature on in-store display form and context by revealing the favorable effects of imaginative displays on customers' purchase behavior (field experiments in Studies 1 and 2). Importantly, we show that this effect can be explained by the dual mechanisms of affect-based arousal (Studies 3a−5) and cognition-based inferred benefits from imaginative displays (Studies 4 and 5). Moreover, we identify congruence between display form and perceived product benefit as a moderator on the main and mediating (i.e., arousal and inferred benefits) effects (Study 5). These findings were obtained using varying forms of imaginative display modeled after actual imaginative displays (Appendix A). The product categories encompassed both utilitarian (i.e., facial tissue, toothpaste, bathroom tissue, and beverage) and hedonic (i.e., chocolates) products for familiar, less familiar, and unspecified brands. In addition, the samples included both actual shoppers (Studies 1 and 2) and online participants (Studies 3a–5) from Australia, the United States, and the United Kingdom, which attested to the robustness of our findings. Taken together, these findings contribute to the in-store display and store atmospherics literature; in addition, they have important managerial implications. Theoretical ContributionsIn extending the in-store display and store atmospherics literature, the present research examines the effects of imaginative displays on customers' purchase behavior. Specifically, such imaginative displays pertain to the domain of display form, while inferred benefits embodied by imaginative displays pertain to the domain of display context. In particular, we reveal that imaginative displays must be both novel and aesthetically appealing. While the use of imaginative displays may appear gimmicky, they can positively influence customers' purchase behavior, product sales, and ROI at relatively low costs.Our findings also extend research on the ensemble effect, which suggests that customers' attitudes toward an ensemble of complementary products can influence their evaluation of the individual product ([36]). We reveal that an imaginative display consisting of multiple units of one product can also increase customers' purchase behavior. By examining the joint effects of novelty and aesthetics, we extend the current literature, which tends to focus on novelty alone, contributing new insights to the product display literature.Prior research on store atmospherics has examined the effects of environmental factors such as music, scent, and color on customer arousal, which in turn enhances their purchase decision ([22]; [25]; [47]). We show that customer arousal can also stem from viewing novel and aesthetically appealing imaginative displays. Besides arousal, we reveal a cognition-based process, whereby themed imaginative displays (i.e., with particular shapes mimicking actual objects such as a bear and a battle tank) convey embodied meanings (e.g., strength and energy) that transfer to the products constituting the display, which increase customers' purchase intention. The dual mechanisms of arousal and inferred product benefits underlying imaginative displays empirically support [ 7] conceptual model of product form. Moreover, we identify the moderating factor of congruence between display form and perceived product benefit, such that congruence will increase customers' purchase intention, while incongruence will lower their purchase intention. We show that arousal has a positive (negative) effect on purchase intention when display form and product benefit are congruent (incongruent), yielding new insights on the polarizing effects of arousal. Practical ImplicationsIn general, retailers benefit from having in-store displays, which can generate unplanned purchases for frequently purchased product categories ([32]). Although retailers are increasingly using imaginative displays in their stores (see examples in the Web Appendix), to our knowledge, prior research has not systematically examined their effects on customers' purchase behavior and store sales. To this end, our two field experiments show that imaginative displays increase product sales while also providing positive ROI for the display. This is borne out by industry practice; for instance, Coke Zero's novel inverted pyramid display increased sales by 13% at select supermarkets implementing the display (see the Web Appendix).Importantly, we reveal that efficacy of the imaginative display is determined jointly by its novelty and aesthetic elements, rather than by its height (Study 2) or the quantity of products in the display (Study 3a). Managerially, an imaginative display offers a cost-effective way to increase sales and ROI compared with a standard display (Study 1). Our conversations with several retailers revealed that some ideas for their imaginative displays came from employees. Thus, it would be beneficial to solicit ideas for imaginative display from employees, who in turn might feel pride when their creations are on display. Obviously, this does not preclude retailers from engaging the services of design professionals. For example, the nonprofit organization Canstruction (canstruction.org) regularly holds exhibitions and competitions of canned food imaginative displays, whereby teams of volunteers, youth groups, and/or Canstruction contractors compete and construct some rather amazing structures, at the end of which all food is donated to local food banks ([26]).We find two mechanisms underlying the effect of the imaginative display: arousal and inferred product benefits. Potentially, the effect of the imaginative display on arousal can be complemented by other contextual stimuli such as congruent music, color, and scent in the store ([22]; [25]; [47]). Moreover, retailers should ensure congruence between inferred benefits from the imaginative display form and perceived product benefit. For example, our imaginative display of a battle tank that embodies strength leads to greater purchase intention for an energy drink that has a congruent benefit but lowers purchase intention for a relaxation drink that has an incongruent benefit. Limitations and Future ResearchNotwithstanding the new insights from the current research findings, we acknowledge several limitations that provide opportunities for future research. First, we note that the product stimuli used in all six studies represent low-involvement packaged goods (i.e., chocolates, bathroom tissue, and toothpaste). It is possible that product involvement could moderate the effect of the imaginative display. For example, would an expensive wine or perfume gain more sales if an imaginative display was utilized? Presumably, a customer seeking to buy a specific fine wine may be less influenced by the imaginative display, but another customer who is uncertain of which wine to purchase may well be persuaded by the display. Second, Study 3b used an incidental technique to manipulate arousal (i.e., external stimulation) rather than task-related arousal (i.e., due to the imaginative display). Prior research suggests that incidental affect and task-related affect could have differential effects ([23]). Thus, future research could determine whether nuanced differences exist between task-related arousal and incidental arousal for imaginative displays.Moreover, it is not clear if our findings would apply to fresh or perishable items such as seafood and vegetables. The product contamination literature ([46]) suggests that some customers may not take well to fresh food items that have been handled by others, particularly if the imaginative display is intricate and takes considerable time to construct. Perceptions of contamination and concerns about product hygiene may lead to undesirable effects ([12]). This conjecture awaits further research.Finally, while we examined imaginative displays that are novel and aesthetically appealing, we recognize that a novel stimulus may also be aesthetically unappealing ([44]; [69]). While [24] suggest that consumers tend to avoid unattractive produce due to altered self-perceptions, they do make some exceptions; for example, ironically, some consumers are embracing ""ugly"" Crocs footwear ([71]). This effect may be moderated by customers' need for uniqueness ([64]). This possibility merits further examination. " 26,Halo or Cannibalization? How New Software Entrants Impact Sales of Incumbent Software in Platform Markets," Platform markets involve indirect network effects as two or more sides of a market interact through an intermediary platform. Many platform markets consist of both a platform device and corresponding software. In such markets, new software introductions influence incumbent software sales, and new entrants may directly cannibalize incumbents. However, entrants may also create an indirect halo impact by attracting new platform adopters, who then purchase incumbent software. To measure performance holistically, this article introduces a method to quantify both indirect and direct paths and determine which effect dominates and when. The authors identify relevant moderators from the sensations–familiarity framework and conduct empirical tests with data from the video game industry (1995–2019). Results show that the direct impact often results in cannibalization, which generally increases when the entrant is a superstar or part of a franchise. For the indirect halo impact, superstar entrants significantly increase platform adoption, which can help all incumbents. Combining the direct and indirect impacts, the authors find that only new software that is both a superstar and part of a franchise increases platform adoption sufficiently to overcome direct cannibalization and achieve a net positive effect on incumbent software; all other types of entrants have a neutral or negative overall effect.","A platform market involves two or more user groups (i.e., sides of a market) whose interactions are mediated through a platform. These markets are typically characterized by indirect network effects, as the actions of agents on one side of the market affect the outcomes of agents on another side ([44]). Many of these markets consist of both platform devices and corresponding software ([24]; [42]). When a new software entrant launches into such a platform market, its ultimate success might depend on a variety of factors or be measured in a variety of ways—sales of the entrant, impacts on sales of incumbent software already on the market, or platform sales. Yet with few exceptions (e.g., [24]; [30]), extant research has focused on sales of the core platform, rather than specifying how an entrant might alter the sales of the existing (i.e., incumbent) software portfolio. In particular, new software might cannibalize incumbent sales ([46]), but it also arguably can create a halo effect that increases the sales of competitive, incumbent software ([32]). These outcomes are critical across the platform ecosystem; as [42], p. 1232) explain, ""Members of a platform ecosystem often have strong vested interest in each other's fates. Because it is the overall appeal of the ecosystem that attracts end users to the platform, the success of individual members depends, at least in part, on the success of other members of the ecosystem—even those with which they may be simultaneously competing.""Consider the paths in Figure 1. A new software entrant can influence incumbent software sales directly (Path A) and positively if the entrant stimulates usage among existing platform owners. However, a negative competitive effect is more likely, as consumers choose to purchase the entrant instead of the incumbent. In addition, the entrant might exert an indirect, positive impact on the incumbent by stimulating sales of the platform (i.e., new platform adoptions) (Path B). Consumers who newly adopt the platform likely backfill their collection of platform-compatible software by purchasing incumbent software (Path C).Graph: Figure 1. Paths by which new software entrants impact sales of incumbent software.But how much of the entrant's impact on incumbent software sales is through direct Path A versus indirect Paths B and C? This central question has not been addressed, and answering it will provide a more holistic view of entrants and their sales implications. While variations of Path B (software → platform sales) have been studied (for a review, see [48]), scholars have ""focused almost exclusively on quantity"" ([29], p. 39) or size of the network. Scholars rarely differentiate the impact of entrants (instead of the quantity of software stock) or new platform adoptions (instead of the quantity of the platform's installed base).In addition, Paths A and C have yet to be broadly addressed. A few studies examine individual software launches but do not quantify the impact on incumbent software. For example, [ 8] study how software entrants impact hardware sales but do not measure whether incumbent software sales are affected; [30] investigates both platform and software sales but assumes no competition between available software. This gap is important because research outside of platforms shows that new products can impact incumbents both positively (e.g., via spillover; [ 1]) and negatively (e.g., by increasing competition; [46]). Given this evidence, we argue that it is vital to understand the holistic impact of new software launches on incumbent software sales.Further, it is important that new product research accounts for the complexities of platform markets, in which entrants influence incumbents both directly (as substitutes or complements [Path A]) and indirectly (through platform demand [Paths B and C]). Such insight is useful, as many new products release into platform markets (e.g., apps, video games); our approach distinguishes the impact of an entrant versus classic software supply. Rather than measure the impact of an increase in overall software stock, we examine the attributes of new software that differentially affect incumbent software (and platform) sales. These findings are not a simple extension of extant knowledge (i.e., new software affects platform sales) into a similar setting. Whereas software entrants are complements to platforms, they are primarily substitutes to incumbent software. Further, in these complex markets, purchases can put customers into active states that prompt them to purchase additional (potentially incumbent) software ([23]). We thus rely on established evidence that platform sales influence software demand, but we go further to detail the specific effects on incumbent software driven by new platform adoptions. In Figure 1, our contributions refer mainly to the understudied Paths A and C (see also Table 1).GraphTable 1. Studies of Indirect Network Effects in Platform Markets. To determine these effects, we use a dynamic model to measure the impact of software entrants on incumbent software sales and platform sales, while also integrating the contextual factors that determine heterogeneous effects. We build on [ 4] model and incorporate heterogeneity through random coefficients, along with fixed effects in a system-of-equations estimation. We use a feasible generalized least squares (FGLS) approach to test how incumbent software–specific factors influence the new product effects. Following [29], we apply these methods to a video game data set, comprising monthly sales of 13,064 games (software) and 19 consoles (platform) over 1995–2019, as well as the dates of software introductions and advertising expenditures. These recent data enable us to report current effect sizes and establish timely evidence for this rapidly evolving industry.Overall, we contribute to research in platform markets by studying the relative impact of new software products on incumbent software sales. We report empirical generalizations for effect sizes for Paths A, B, and C, distinguishing direct from indirect impacts. With our holistic approach, we are then able to quantify the total (net) impact of an entrant by aggregating the effects of each path. In determining effects, we add context by identifying characteristics of entrants and incumbents that moderate each path. Drawing from the sensations–familiarity theoretical framework ([25]), which fits well for hedonic software (e.g., apps, video games), we operationalize software characteristics by capturing whether the software is a superstar or member of a franchise.We find that the direct impact (Path A) of an entrant usually, but not always, results in cannibalization, depending on its characteristics. When entrants are superstars and/or members of a franchise (vs. not a superstar or franchise member—""standard software"" hereinafter), they directly cannibalize incumbents more. The incumbents' characteristics offer little protection against such cannibalization. We find that new superstars significantly increase new platform adoption through the indirect Path B, which benefits incumbent superstar software and franchise members more than standard software (Path C). By combining both the indirect and direct impacts, we determine the net overall impact: Standard entrants hurt all incumbents, but a new superstar can produce a net positive halo, depending on the context. For example, a 1% increase in entrants with both superstar and franchise status drives a.0207% net increase (direct + indirect) in the sales of incumbent franchise software; the direct cannibalization of incumbent sales (via Path A) of −.0130% is overcome by a halo effect from new platform adopters (via Paths B and C) that indirectly increases incumbent sales by.0337% (.0207 = −.0130 + .0337). We show managers how to estimate both direct and indirect impacts of different types of new software based on the specific makeup of a firm's portfolio. We also show how these estimates translate to financial outcomes. We provide new insights for platform markets and new product research, while extending the sensations–familiarity framework. ConceptualizationTo connect our findings to extant literature, we ground our conceptualization in network effects research. We then outline relevant theory from which we identify key variables. Network Effects in Platform MarketsIndirect network effects in systems markets are of enduring interest to scholars (e.g., [12]; [45]; [48]). Systems or platform markets often comprise hardware, such as a computer, game console, or smartphone, and related hardware-compatible software, such as computer programs, video games, or apps. A systems logic applies to digital platforms too (e.g., Netflix); so, to ensure the broad applicability of our study logic, we use the terms ""platform"" (vs. hardware; [47]) and ""software.""Platform markets create both direct and indirect network effects. Direct network effects arise when the value to a user increases with the number of other platform users; for example, a multiplayer online game with many fellow players is more desirable ([33]). Indirect network effects exist if the number of platform users entices software producers to create new offerings for the platform. As the software portfolio improves (e.g., quality, variety), so does the attractiveness of the platform to users ([12]). Indirect effects apply to all members of the system: platforms need an attractive software portfolio to attract customers, and software providers prefer to develop software for platforms with large installed bases. In the vibrant research dedicated to these markets, briefly summarized in Table 1 (for a more expansive list, see Table W1 in the Web Appendix), we know of no efforts to calculate the extent of the total impact (direct and indirect) of new software entrants on sales of incumbent software. Sensations–Familiarity FrameworkMany platform markets exist for entertainment products (e.g., video games, movie streaming services, apps), so we rely on a theory that predicts consumer responses to hedonic products, namely, the sensations–familiarity framework ([25]). However, a similar logic can apply in nonentertainment settings (e.g., [38]). According to this theory, a person consumes hedonic products for pleasure (vs. functional benefits) that can be derived from the very act of consumption. This consumption act (e.g., playing a game) generates emotional responses (e.g., happiness, melancholy) and cognitive responses, which define people's mental representations of the experience. [ 6]) argue that consumers combine these responses to form holistic product judgments (vs. evaluating the attributes piecemeal). The impact of sensation and familiarity on consumersThe sensations–familiarity framework indicates that both sensations and a sense of familiarity can each drive emotional and cognitive responses that underlie judgment. Sensations are physiological (vs. purposive) responses, felt as a sense of arousal when nerves are stimulated and hormones (e.g., dopamine) released. Humans become satiated easily and prefer novel, multidimensional sensations, which leads to an innate desire to seek out rich, new sensations instead of repeating the same one ([35]). Familiarity refers to a consumer's sense of connectedness to a product (or its elements). Built through prior exposure, when a new product includes elements that are familiar, the sense of familiarity triggers memories and emotions that transfer to the new product ([ 9]). Familiarity also enhances processing fluency by helping consumers quickly make sense of new products ([39]).Sensations and familiarity thus form a delicate balance. A new product that is too familiar (few new sensations) seems stale and unappealing. A new product that is too novel (stark new sensations with little link to the familiar) can overwhelm consumers by the sheer intensity of sensations (e.g., never-ending explosions in a Michael Bay movie; [25]). Thus, new product managers introducing hedonic products aim to provide the ""right"" levels of sensations and familiarity to maximize the products' attractiveness. Relevant variablesThe variables we derive from this framework in turn reflect familiar elements of prior products and/or promise rich new sensations. For video game software, the task is to offer enough familiarity to connect with consumers (e.g., familiar worlds, product designs, characters) while also providing new sensations to arouse consumers (e.g., exciting new patterns of play, beloved characters in new worlds, novel designs) ([25]). Thus, we focus on two variables with distinct influences in extant studies. First, software products (e.g., video games, apps, movies) of extraordinarily high quality offer rich, desirable sensations. We thus operationalize the sensations factor as ""superstar software,"" defined as ""software titles of exceptional high quality"" that often ""possess unique and superior attributes"" ([ 8], pp. 88–89). Superstars may achieve high payoffs, but this term refers explicitly to product quality, unlike the terms ""blockbusters"" or ""hits,"" which reflect sales volume known only after product release ([ 8]). Second, we operationalize familiarity as software that is part of a franchise (e.g., Super Mario Bros., Star Wars). Extensions of a franchise leverage the influence of familiarity, but they also offer some new sensations and contexts (e.g., Super Mario Bros. 2, The Empire Strikes Back). By integrating elements of an existing brand into additional products, ""the resulting set of products, in its entirety, then constitutes the 'franchise'"" ([25], p. 429). Accordingly, software that is part of an established franchise should be desirable to consumers ([40]), and real-world evidence consistently shows that franchise products (e.g., sequels, prequels, reboots/remakes) often outsell similar, nonfranchise products ([33]). Both superstar and franchise variables have appeared in prior studies, but we do not know of their use to determine how new product entrants affect incumbent sales. Next, we explicate the roles these variables play in the three paths of Figure 1. Path A: The Direct Impact of New Software Entrants on Sales of Incumbent SoftwareIn Path A—the direct impact of new software entrants on sales of incumbent software—we are conceptualizing the behaviors of existing platform owners. In this case, a new software entrant is a potential substitute for incumbent software ([14]); they all compete for the consumer's (i.e., platform owner's) limited budget ([18]). Thus, to accurately quantify Path A, we should consider not only average effects but also how effects differ according to the characteristics of both the new entrant and the incumbent.The characteristics of the new entrant should be particularly powerful, as the effects suggested by the sensations–familiarity framework may be most influential when consumers encounter new stimuli. For example, when they first play a new video game, the sensations offered are, to some degree, new to the world; if these novel sensations also are rich, as in the case of superstar software, they likely exert the strongest impacts on consumer preference. However, product newness also evokes uncertainty, so familiarity likely shapes consumers' responses as well. Being part of a franchise offers a comforting connection to known objects, such that relevant memories and emotions can transfer to the new entrant. Thus, we expect characteristics of both superstar and franchise status to increase the direct cannibalization caused by a new entrant.In Path A, which reflects the consumer's choices between the new entrant and the incumbent, the characteristics of the incumbent software might matter too, but their power is less clear. At first glance, the rich sensations offered by a superstar incumbent might enhance its attractiveness and provide some level of protection against cannibalization by standard new entrants. Likewise, the familiarity benefits of being part of a franchise should leave the incumbent from a franchise (vs. nonfranchise) less susceptible to cannibalization by new entrants. However, prior applications of the sensations–familiarity framework have focused almost exclusively on new products ([ 5]), so it is not entirely clear how the findings generalize to incumbents. Being a superstar or part of a franchise may not provide incumbents with protection from competition. First, the specific sensations offered by this incumbent would be new to consumers who had yet to purchase it, but they are not new to the world. Even new purchasers likely have gained some predictive information about them, such as through word of mouth from other consumers with experience. Second, word of mouth about these experiences reduces not only novelty but also the level of uncertainty for new consumers, which might attenuate the familiarity benefit of being a franchise incumbent. Path B: The Direct Impact of New Software Entrants on Platform AdoptionIt is well known that new software influences sales of platform devices (for a review, see [41]]). We test this path to ensure a complete model and to isolate the indirect impact of new software entrants on sales of incumbent software by stimulating platform sales (Paths B and C). However, we differ from extant literature by focusing on the impact of new software entrants (vs. software stock) on new platform adopters (vs. installed base). That is, we expect software to increase platform adoption, consistent with traditional network logic, which may benefit incumbents, but we also test for distinct effects of new entrants. For example, because new superstars create excitement, they might attract increased attention and more new platform adopters ([ 8]; [21]). Franchise video games also are disproportionately attractive to consumers, so they prompt higher sales ([16]; [33]), and new franchise software might draw more new adopters to the platform. Path C: The Impact of New Platform Adopters on Sales of Incumbent SoftwareWhereas Path A taps how a new software entrant competes directly with incumbent software among existing platform owners, Path C represents sales of incumbent software generated by new platform adoptions and thus reflects consumers' incumbent-versus-incumbent choices upon adopting the platform—specifically, whether and which incumbent offerings to buy. We thus calculate how the influx of new adopters affects demand for incumbent software (Path C), independent of the direct competitive effect of new software entrants.Traditional cannibalization viewpoints frame a market as a zero-sum game with a finite number of customers. But in platform markets, a new product can help other products by driving increased traffic ([18]; i.e., product spillover effects; [ 1]). If a new software entrant induces new consumers to adopt the platform, they might buy other software from the existing portfolio. For example, if PlayStation loyalists wanted to play Super Smash Bros. Ultimate, released on December 7, 2018, and therefore bought a Nintendo Switch console to gain access to it, the consumers likely bought other Switch games to make full use of the console's capabilities (e.g., on-the-go gaming). The players also might try to ""backfill"" a collection of software favorites, played previously on the PlayStation; such cross-platform adoption is common for entertainment products ([30]).The notion that platform sales lead to software sales is foundational to network effects theory, but we extend this traditional view. That is, our argument is not about new platform adopters in general, but on the purchases of new adopters attracted by the release of new software. We study Path C as part of our novel effort to understand the totality of Paths B and C. Further, we know of no other study that measures whether new platform adopters influence incumbent software sales immediately, that is, in the same month as platform adoption, when new adopters' budgets are likely depleted from buying the platform and new software entrant. Studies that span the platform's entire installed base cannot specify how platform adoption affects software sales immediately, because they lump existing platform owners in with new adopters. Assessments of software stock overall, rather than individual incumbent software, also cannot determine which type of software (e.g., superstar, franchise) benefits the most from greater platform adoption.For Path C, we again apply the sensations–familiarity framework: for choices between two or more incumbent products, superstars and franchise software are more attractive than standard incumbents, so their sales may benefit most from new platform adopters. Overall (Net) Impact of New Entrants on IncumbentsFinally, to quantify the total net impact of a new software entrant on sales of incumbent software, we combine the direct (Path A) and indirect (Paths B and C) impacts. We want to understand in what circumstances a positive halo effect (i.e., increasing the number of platform adopters who subsequently buy incumbent software) is enough to overcome the direct cannibalization effect caused by increased competition.On the one hand, a new product could be so keenly attractive that it cannibalizes sales of the incumbent ([46]), despite the overall increase in platform customers ([14]). With limited budgets, consumers cannot purchase an infinite amount of software; in turn, the cannibalization argument suggests that new software entrants prompt customers to choose them instead of an incumbent, resulting in a negative net impact on sales of incumbent products. On the other hand, the new entrant might attract enough new adopters, who subsequently purchase incumbent software, that it overcomes the direct competitive effects, resulting in a positive net impact. We note that [24] find that platforms with newer customers sell more content.To reconcile these competing ideas, we use a decision rule suggested by [11]: to increase net software demand, the network effect of new platform users must be greater than the competitive effects among software products. With empirical tests, we can determine the sizes of both the direct cannibalization impact (Path A) and the indirect impact (Paths B and C) from new platform adopters, then calculate the overall net impact. But we also consider the characteristics of the new software entrant that might determine the strength of the direct and indirect impacts. Any new entrant increases the size of the software portfolio and thus should incrementally increase platform attractiveness and sales ([48]); however, to overcome cannibalization, the new software entrant needs to generate so many new platform adopters, who then purchase so much incumbent software that they create an overall net halo effect ([11]). It is likely that only software that is highly valued will attract a sufficient number of new platform adopters to overcome cannibalization. For example, prior research has demonstrated that superstar software produces a large surge in platform sales ([ 8]). Therefore, we measure if Paths B and C produce enough incremental sales to overcome Path A, and if this total effect differs based on superstar and franchise status. Control variablesProduct-level competition ([18]) implies cannibalization due to substitution ([14]). Well-established factors that affect substitutability include sequel status, genre, price, exclusivity, and advertising ([25]; [33]; [42]). We control for these either directly or through use of fixed effects. MethodologyOur empirical context is the console-based video game industry, a large and significant part of the economy. Games for video game consoles generate more than $34 billion in annual sales. The entire industry is larger, including over $32 billion earned annually by computer-based and online games; mobile-device games earn over $70 billion annually ([51]).Our setting is generalizable to other networked markets and is ideal for exploring our research questions for several reasons. First, studies in marketing (e.g., [ 8]; [29]) and related fields (e.g., [13]; [23]) use video game data to test platform market theories because video games are ""a classic example of a high-tech networked market"" ([20], p. 284). Second, managing product introductions is an important decision in networked industries ([31]), especially for video game firms ([ 8]). These markets observe frequent new product introductions and offer the ability to observe many products from inception to decline ([10]). Third, games are not natural complements; owning one game does not increase the utility of owning another, reducing confounds when examining cannibalization and halo effects. Fourth, characteristics in gaming (e.g., superstar status, franchise membership) map directly to the variables suggested by the sensations–familiarity framework ([ 8]; [25]; [30]).We obtain monthly quantity and revenue data for 8,470 unique games and 19 consoles on the U.S. video game market from January 1995 through June 2019 from the market research firm The NPD Group. Many games are released on multiple consoles, creating 13,064 software/platform combinations. We structure the data as an unbalanced panel with 698,703 software/platform/month observations. The average console is on the market 100.37 months, for 1,907 platform/month observations. We match NPD data to advertising data from Kantar Media. These data include U.S. radio, television, cinema, online, outdoor, and print advertising spend for each game and each console in each month. We aggregate and obtain advertising expenditure at the software/platform/month level for games and at the platform/month level for consoles. Key Definitions and Operationalizations New versus incumbent softwareWe classify software as new in the first month we observe sales on the platform. Software is an incumbent any time after its first month on the platform. FranchisesWe operationalize familiarity with game franchise status. NPD gives a game's franchise affiliation (e.g., Call of Duty) when applicable. This identifies the initial entry in the franchise along with any extensions. We use this to classify whether incumbent or new software belongs to a franchise. Note that the first entry in a franchise is not a franchise when it is introduced; rather, it becomes a franchise when the first extension is introduced. Overall, 62.5% of software/platform observations are connected to a franchise in some way: 17.0% are first entries that become part of a franchise when an extension is introduced, and 45.5% are franchise extensions. SuperstarsWe use superstar status to identify software that provides rich sensations. We assess superstar status using critic evaluation, similar to other game industry studies ([ 8]; [33]). Importantly, critics typically review and provide assessments before a game's release ([53]). This means that the quality measure is determined independently from sales, which lessens endogeneity concerns. We obtain data from Moby Games (https://www.mobygames.com/), which has track record in video game research (e.g., [15]). Moby Games provides a Mobyrank, or average critic rating, for each software/platform observation. Mobyrank ranges between 0 (low) to 100 (high); we classify superstars as games with Mobyrank ≥ 90, similar to other studies ([ 8]; [21]; [26]). This operationalization has external validity; it is a popular industry cutoff as game-developer bonuses are often tied to achieving average critic scores of 90+ ([49]). We use superstar-franchise to label software that are both superstars and part of a franchise. In total, 2.427% of software/platform observations involve superstars, similar to other video game studies ([ 8]; [21]); 1.85% are superstar-franchise; and.57% are superstars that are not a part of a franchise. New Platform Adopters Model: Variable Operationalization and Equations Dependent variableFor Path B, our goal is to estimate the effect of new software entrants on new platform adopters. We log-transform the continuous variables when estimating our model because we compare new platform adopters (and software sales) among a disparate set of platforms (and software) that have differing sales volumes ([ 8]). We operationalize new platform adopters as the natural log of unit sales of platform j at time t ( lnNewPlatAdoptersjt ) using each platform's monthly sales. Independent variables lnIntSoftjt , lnIntSuperjt , lnIntFranjt , and IntSuperFranjt are the natural logs of the count of all, superstar, franchise, and superstar-franchise software new to platform j at time t, respectively. Note that assessing the impact of superstar-franchise software with a classic interaction effect using lnIntSuperjt and lnIntFranjt is incorrect because these are binary, software-level characteristics. Rather, superstar-franchise classification occurs at the software level before creating the aggregate variables at the platform level. In addition, we use mutually exclusive classifications of superstar, franchise, and superstar-franchise to aid in interpretation. ControlsWe include five controls that influence platform demand. First, the natural log of price of platform j in time t, lnPricejt , adjusted for inflation using the Consumer Price Index for All Urban Consumers (CPI-U) with January 2007 = 100. Second, we control for the stock of available software ([17]) for each group ( lnAvailSoftjt , lnAvailSuperjt , lnAvailFranjt , lnAvailSuperFranjt ). Stock is measured using the natural log of the count of the active software catalog (i.e., software for platform j introduced prior to t with positive sales in the period). This enables us to distinguish the effect of the software entrant variables from software stock. Third, we use natural log of advertising for platform j in t, lnPlatAdvjt , adjusted for inflation using the CPI-U ([45]). Fourth, we include platform age ( PlatAgejt ) as months since introduction, and its square, because evidence suggests that the effect of age on platform demand is nonlinear ([26]; [30]). Fifth, month and year fixed effects address month-specific (e.g., holidays) and year-specific (e.g., economic shocks) trends.Finally, we add 1 before logging the software entrant, software stock, and advertising variables to ensure that the log-transformation is defined when the level is 0. Platform adoption equationPrevious research finds that platform sales are influenced by lagged realizations of independent variables ([ 8]), so we use a dynamic model where lnNewPlatAdoptersjt depends on previous values ([52]): lnNewPlatAdoptersjt=βIntSoftlnIntSoftjt+βIntSuperlnIntSuperjt+βIntFranlnIntFranjt+βIntSuperFranlnIntSuperFranjt+∑p=1PβlagplnNewPlatAdoptersjt−p+Controlsjt+εjt, Graph( 1)with Controlsjt=βPricelnPricejt+βASoftlnAvailSoftjt+βASuperlnAvailSuperjt+βAFranlnAvailFranjt+βASuperFranlnAvailSuperFranjt+βPlatAdvlnPlatAdvjt+βAgePlatAgejt+βAge2PlatAgejt2+MonthDummiest+YearDummiest+νj. Graph MonthDummiest and YearDummiest represent coefficients with their respective dummies for the month and year in time t. We find the appropriate lag length (P = 1 in our case) by experimenting with longer lags and eliminating those that are insignificant ([19]). Including lags allows changes in variables to affect future periods; the coefficients are current-period effects; multiplying by 1/(1−∑βlagp) gives total long-term effects ([19]).Our log-log specification means that the coefficients on the software entrant variables are elasticity estimates. Specifically, βIntSoft captures the elasticity of platform adoption with respect to entry of standard software. The mutually exclusive classification means that βIntSuper , βIntFran , and βIntSuperFran are the moderating effects for superstar entrants that are not part of a franchise, franchise entrants that are not superstars, and entrants that are both superstars and part of a franchise, respectively.We also include platform fixed effects, νj , to control for time-invariant, platform-specific heterogeneity (i.e., individual console characteristics) that impacts adoption rates. We identify coefficients from within variance given the fixed-effects approach. We display panel-level descriptive statistics, which show measures of within variance for each variable, in Web Appendix Table W2. Table 2, Panel A, summarizes variables and definitions for the platform equation; it also includes variables that we define when discussing endogeneity.GraphTable 2. Select Variable Names and Definitions. 1 aCorrected for inflation using the CPI-U, with January 2007 = 100. Incumbent Software Sales Model: Variable Operationalization and Equations Dependent variableFor Paths A and C, our goal is to estimate the effect of new software entrants and new platform adopters on sales of incumbent software. We operationalize sales of incumbent software as the natural log of sales of software i, corresponding to platform j, at time t ( lnSoftSalesijt ). We measure all incumbent software on the market at time t that meet two criteria: ( 1) the software has observed sales in t and ( 2) the platform on which the software is available also has observed sales in t. The first condition ensures that we measure only active software. The second ensures that we observe the indirect (through platform sales; i.e., Path C) and direct (through competition; i.e., Path A) effects of new software on the sales of each incumbent. Independent variablesTo measure the direct impact of a new entrant via Path A, we include the natural log of the count of new entrants ( lnIntSoftjt , lnIntSuperjt , lnIntFranjt , and lnIntSuperFranjt ) on console j at time t, operationalized as in the platform equation. However, as discussed in the conceptualization, a new entrant affects sales differently when the incumbent is in the same genre ([50]); competition will be less salient for incumbents in different genres. Thus, as controls we include the natural logs of the count of the entry variables in a different genre than incumbent software i (e.g., lnIntSoftDifGenrejt ).[ 5]To assess the indirect impact of an entrant via Path C, we include the natural log of platform sales in time t, lnNewPlatAdoptersjt , the dependent variable from the platform equation. ControlsWe separately include the platform's installed base, operationalized as the natural log of cumulative sales of platform j prior to t ( lnPlatIBjt ), to allow for differential effects of new platform adopters and previous platform adopters on incumbent software sales. We also include eight additional groups of control variables. First, we include the natural log of average price of software i on platform j in t adjusted for inflation using the CPI-U, lnPriceijt ([30]). Second, a dummy variable ( SequelIntroedijt ) takes the value 1 if any software entrant on platform j in t is a franchise extension to incumbent software i ([33]) to control for any differential effect a new entrant may have on incumbents from the same franchise (e.g., the introduction of Tomb Raider 2 may be more likely to cannibalize incumbent Tomb Raider compared with other entrants). Third, we use the natural log of the active catalog of software for platform j similar to the platform equation, but we exclude incumbent software i from the count (e.g., lnAvailSoftijt ). We add the natural logs of the active catalogs in a different genre than incumbent software i (e.g., lnAvailSoftDifGenreijt ) to control for the effect of competition within and outside the genre. Fourth, we include software installed base, operationalized as the natural log of sales of software i on platform j prior to t, lnSoftIBijt ([53]). Fifth, we include the natural log of advertising expenditure for software i in time t, adjusted for inflation using the CPI-U, lnSoftAdvit . We also include lnPlatAdvjt to address any platform advertising spillover to software sales. Sixth, we utilize software/platform specific effects ( μij ) to account for software-/platform-specific variation. Seventh, period fixed effects address shocks common across all software/platform observations specific to any month from January 1995 to June 2019. Lastly, we measure the age of software i on platform j ( SoftAgeijt ) in months since introduction on the platform. Note that period fixed effects are perfectly collinear with the trend captured by software age. However, we include software age squared ( SoftAgeijt2 ) because game sales show an exponential decline with age ([10]). Period fixed effects are not perfectly collinear with SoftAgeijt2 because software enters at different periods throughout our time frame.Similar to the platform equation, we add 1 before logging the software entrant, stock, and advertising variables to ensure that the log-transformation is defined when the level is 0. Incumbent software heterogeneityWe are interested in the effect of incumbent superstar, franchise, and superstar-franchise status on the impact of new software entry and new platform adopters. We let Superij  = 1 if software i on platform j is a superstar but not part of a franchise, 0 otherwise; Frani  = 1 if it is part of a franchise but not a superstar; SuperFranij  = 1 if it is both a superstar and part of a franchise. Incumbent software sales equationThe functional form for the software equation is lnSoftSalesijt=δIntSoftijlnIntSoftjt+δIntSuperijlnIntSuperjt+δIntFranijlnIntFranjt+δIntSuperFranijlnIntSuperFranjt+δNewAdoptersijlnNewPlatAdoptersjt+∑s=1SδlagslnSoftSalesijt−s+Controlsijt+ηijt, Graph( 2)where δGSij=θGS+θSuperGSSuperij+θFranGSFrani+θSuperFranGSSuperFranij+GenreDummiesi+νjGS+ωijGS Graph( 3)for GS  =  IntSoft , IntSuper , IntFran , IntSuperFran , and NewAdopters and Controlsijt=δPricelnPriceijt+δSequelIntSequelIntroedijt+δIntSoftDifGenrelnIntSoftDifGenreijt+δIntSuperDifGenrelnIntSuperDifGenreijt+δIntFranDifGenrelnIntFranDifGenreijt+δIntSuperFranDifGenrelnIntSuperFranDifGenreijt+δASoftlnAvailSoftijt+δASuperlnAvailSuperijt+δAFranlnAvailFranijt+δASuperFranlnAvailSuperFranijt+δASoftDifGenrelnAvailSoftDifGenreijt+δASuperDifGenrelnAvailSuperDifGenreijt+δAFranDifGenrelnAvailFranDifGenreijt+δASuperFranDifGenrelnAvailSuperFranDifGenreijt+δSoftIBlnSoftIBijt+δPlatIBlnPlatIBjt+δSoftAdvlnSoftAdvit+δPlatAdvlnPlatAdvjt+δSoftAge2SoftAgeijt2+PeriodDummiest+μij. Graph PeriodDummiest represents coefficients with their respective dummies for the period in time t. We include lags of the DV for a dynamic specification and follow [19] for the appropriate lag length (S = 1 in our case). As with the platform equation, the mutually exclusive classification of software along with the log-log specification means the coefficient on lnIntSoftjt is the elasticity of incumbent software sales with respect to software entry; the coefficients on the other software entry variables are moderation effects. Similarly, the coefficient on lnNewPlatAdoptersjt is the elasticity with respect to new platform adopters.Importantly, including the new entrant variables and new platform adopters in Equation 2 means we can interpret their impacts holding the other constant. The effect of the new entrant variables via Path A is their impact on incumbent software sales independent of new platform adoption (i.e., it represents sales to prior platform adopters). The impact of new platform adopters via Path C represents incumbent software purchases by those who just adopted the platform. Connecting Path C with Path B means that Path B–C represents the impact on incumbent software sales from new adopters spurred by new software entrants.Note the relationship between Equations 2 and 3. The coefficients δIntSoftij , δIntSuperij , δIntFranij , δIntSuperFranij , and δNewAdoptersij from Equation 2 are random and allow for heterogeneous effects of the entry variables and platform adopters on each incumbent. Equation 3 specifies that the random coefficients are determined by the incumbent characteristics ( Superij , Frani , SuperFranij ), software genre fixed effects, and platform fixed effects at the software level ( νjGS ).[ 6]Our mutually exclusive classification for entrant and incumbent software means the constant in the estimation of δIntSoftij using Equation 3, θIntSoft , is the base impact that standard software entry has on incumbent software sales via Path A. The constants in the estimates for the other entry variables ( θIntSuper , θIntFran , θIntSuperFran ) show how the base impact is moderated when the new entrant is a superstar, franchise, or superstar-franchise software. The coefficients on Superij , Frani , and SuperFranij capture the moderating effect of the incumbent characteristics.Similarly, the constant in the estimation of δNewAdoptersij is the base impact of new platform adopters on standard incumbent sales via Path C; the coefficients on incumbent characteristics are moderating effects. Subsequently, we discuss how we combine estimates from all equations to obtain the overall net (direct + indirect) impact. We display panel-level descriptive statistics in Web Appendix Table W3. Table 2, Panel B, presents variable names and definitions. Estimation and Results Key Issues and Overall ApproachOur estimation framework addresses several issues. First, platform and software sales form a system of equations. New platform adoption and incumbent software sales are determined simultaneously; platform sales also affect the market potential of incumbent software. We jointly estimate Equations 1 and 2 to address this issue and improve efficiency by taking into account the likely correlation of error terms.Second, we allow for parameter heterogeneity in Equation 2 with random coefficients at the software/platform level. Unfortunately, time-invariant software-/platform-specific characteristics (e.g., software exclusivity, quality, release date) likely correlate with time-variant variables (e.g., software price, advertising) so a traditional hierarchical linear approach, where μij is modeled as random, will produce biased estimates. To address this, we use fixed effects for μij along with the method from [ 4] to incorporate random coefficients. However, we expand their method to consider a system of equations with endogenous variables. The Web Appendix includes the derivation along with a detailed step-by-step guide to implementation.We recover unbiased estimates of δIntSoftij , δIntSuperij , δIntFranij , δIntSuperFranij , and δNewAdoptersij for each software/platform panel as part of the procedure. We then run auxiliary regressions of Equation 3 to find the impact of incumbent software characteristics on the random coefficients. [52] notes that a more efficient FGLS estimator is available when regressing heterogeneous coefficients in random parameter models like ours. We derive and use the FGLS estimator for Equation 3 (for details, see the Web Appendix). Endogeneity and InstrumentsThere are several endogeneity concerns in the platform and software equations. We use instruments that have a track record in the literature (e.g., [17]; [27]) or are derived from best practice ([22]; [36]). We give a detailed description of endogeneity concerns, instruments, and first-stage estimations in the Web Appendix (Tables W4 and W5). We emphasize that the usual diagnostics are always satisfied (e.g., first-stage F-stats > 10; insignificant Hansen's J). We briefly discuss the validity of key instruments used to identify our endogenous variables. New platform adoptersFirst, the software entry variables are endogenous in the platform equation. A platform becomes more attractive to software providers when more consumers adopt it; the platform becomes more attractive to consumers as it introduces more software ([48]). The theory of indirect network effects suggests platform installed base as a valid and relevant instrument. Platform installed base satisfies the exclusion restriction because its impact on new platform adoption is indirect, through its influence on software provision ([12]; [30]).Second, platform price and advertising are likely endogenous as managers consider unobservables when choosing strategies. We include producer price indexes (PPIs) from related industries to capture cost shocks that impact pricing decisions ([17]; [21]). PPIs are valid because they are not set considering unobservables that impact platform demand ([36]). We interact the PPIs with platform characteristics and other instruments to aid in identification and create platform-specific instruments ([43]).In addition, we use the average age of nonsuperstars in the active catalog to instrument for price and software entry, similar to [27]. Firms likely compensate for old catalogs with lower prices and new software. This instrument is valid if consumers are unaware of all nonsuperstar release dates when considering platform purchase, which is likely because consumers are typically unfamiliar with nonsuperstars ([ 8]).We use the average advertising expenditure of each software available on competing platforms as an instrument for platform advertising. This is valid because individual software on competing platforms is unlikely to take into account unobservables that impact focal platform demand in a coordinated and systematic fashion when choosing advertising strategy. It is relevant because it is related to software-level advertising on the focal platform ([36]) and captures relative investments in software versus platform advertising. Incumbent software salesTime-invariant software-/platform-specific characteristics (e.g., software exclusivity, quality, entry timing) likely correlate with other variables in Equation 2 (e.g., price). Fixed effects address this, as these characteristics do not change over the software's life. In contrast to the new platform adopters estimation, traditional fixed effects produce Nickell bias ([37]) in dynamic models when N ( = 13,064) is large relative to T ( = 53.48, on average).[ 7] Instead, we first-difference Equation 2 and instrument with lagged levels of the dependent variable ([ 3]).We do not consider new platform adopters or software entry to be endogenous. It is unlikely that all platform adopters in a particular period act in a coordinated fashion and account for unobservables of a particular incumbent software. Similarly, it is implausible that managers coordinate software entry systematically and consider all the individual software/platform/month unobservables that affect the performance of all software already on the market.However, software price, software advertising, and SequelIntroedijt likely are endogenous. Price and advertising strategies may account for unobservables that impact software sales; the launch of a franchise sequel may relate to unobservables of franchise members on the market.We exploit the panel nature of the data to construct instruments for price and advertising using values from other software on the platform when they were the same age as the focal software. For example, for advertising, if software is six months old, we calculate the variable from the advertising expenditure of previously released software when it was six months old. These instruments are relevant because they capture pricing and advertising trends based on software age. Similar instruments have been used in studies of movies ([28]) and games ([30]); they are valid, because it is unlikely that managers choose their software pricing and advertising strategies considering future unobservables of yet-to-be-released software.We use two instruments for SequelIntroedijt that capture trends in franchise extension introduction for similar software. First, we use the percentage of franchise software introduced out of all software introduced on the platform in the focal software's genre prior to the current period. This is relevant as it captures past trends in franchise entrants for similar software; it is valid as it is unlikely that managers coordinate franchise software entry in a systematic fashion accounting for future unobservables for all other software in the genre. Second, we use the natural log of the number of franchise extension introductions in the current period on different platforms but in the same genre as the focal software. This is valid, as it is unlikely that software on different platforms takes into account unobservables that influence focal software performance in a systematic and coordinated fashion; it is relevant because it captures franchise introduction intensity of similar software in the current period. Estimation Results and DiscussionWe assess Paths A, B, and C using the joint generalized method of moments estimation of the platform-adoption and incumbent-software sales equations, along with the heterogeneous-coefficients equation. (See estimated variance/covariance of the heterogeneous coefficients in Web Appendix Table W6.) Results for Path AWe first turn to the estimation of Path A, which captures the direct competitive impact new entrants have on incumbents. The full results for estimating incumbent software sales (Equation 2) are shown in Table A1 in the Appendix. While Table A1 is useful for understanding the impact of the control variables and model diagnostics, the coefficients for the new entry variables are not as meaningful because they are simply the average of the heterogeneous effects and do not address our research questions. The full heterogeneous effects, showing the results of every combination of incumbent/new entry characteristic (i.e., higher-order interactions), are provided in Table A2 in the Appendix. To simplify our presentation, Table 3 shows key results from Table A2 relating to the base impact that standard new entrants have on standard incumbents. Panel A shows how entrant type moderates the base effect. Entrants with superstar status and/or franchise affiliation increase their competitive edge over incumbents. While entry by standard software negatively impacts incumbent sales (β = −.0110, p < .01), the effect is larger when the new software is a superstar (β = −.0074, p < .10), part of a franchise (β = −.0128, p < .01), or both (β = −.0058, p < .05). New entrants that exhibit rich sensations and/or leverage familiarity hurt standard incumbents' sales more. However, familiarity in an entrant does not augment the impact of rich sensations; the effect of a superstar entrant is not significantly different from a superstar-franchise entrant (z = .3127, p > .10).GraphTable 3. Path A: Direct Impact of Software Entry on Incumbent Software Sales. 2 *p < .1.3 **p < .05.4 ***p < .01.5 Notes: SEs robust to platform clustering are in parentheses.Panel B shows how incumbent characteristics moderate the impact of a standard entrant. The negative impact of a standard entrant is not attenuated if the incumbent is a superstar (β = −.0208, p > .10), part of a franchise (β = .0013, p > .10), or both (β = .00043, p > .10). Incumbents that possess rich sensations and/or familiarity do not have additional protection from direct cannibalization caused by new entrants. In addition, the combination of rich sensations and familiarity in an incumbent does not offer extra protection compared with rich sensations alone; the impact on superstar incumbents is not significantly different from the impact on superstar-franchise incumbents (z = 1.1209, p > .10). Results for Path BThe combination of Paths B and C is the indirect impact of software entry on incumbent software sales via new platform adoption. Considering Path B first, we show the platform adoption estimation (Equation 1) in Table 4. Consistent with prior research (e.g., [ 8]), we find that only new entrants with superstar status affect platform adoption. While entry by standard software does not have a significant impact (β = −.3935, p > .10), the effect is positively moderated when entrants are either superstar (β = 2.7717, p < .01) or superstar-franchise (β = 5.3839, p < .01). Combining the base and moderating effects, we find that new superstars (β = −.3935 + 2.7717 = 2.3782, p < .01) and new superstar-franchise (β = −.3935 + 5.3839 = 4.9904, p < .01) entrants positively influence platform adoption. The coefficient on superstar-franchise entrants is greater than the coefficient on superstar entrants (z = 1.7831, p < .10), which suggests that franchise status augments the effect of new superstars. However, franchise status alone does not increase platform adoption (β = −.2700, p > .10). Our results suggest that only new entrants with superstar status will impact new adopters enough to spur an indirect halo effect on incumbents.GraphTable 4. Path B: Impact of Software Entry on Platform Adoption. 6 *p < .1.7 **p < .05.8 ***p < .01.9 Notes: SEs are robust to platform clustering; DV =  lnNewPlatAdoptersjt ; first-stage F-statistics for endogenous variables lnIntSoftjt , lnIntSuperjt , lnIntFranjt , lnIntSuperFranjt , lnPricejt , and lnPlatAdvjt are all >10. First-stage estimates are shown in Web Appendix Table W4. Results for Path CPath C considers the impact of new platform adopters on incumbent software sales. Table 5 displays the results of the heterogeneous coefficient estimation for new platform adopters (from Table A2). Standard incumbents benefit from new platform adopters (β = .0024, p < .10). However, incumbents who are superstars (β = .0340, p < .01), part of a franchise (β = .0043, p < .05), or both (β = .0153, p < .01) benefit more. Furthermore, superstar status matters more than franchise status: the impact of new platform adopters on superstar and superstar-franchise incumbents is larger than franchise incumbents ( χ2(2)  = 6.0213, p < .05). However, franchise status does not augment the benefit to incumbents with superstar status. Our results suggest that incumbent superstars benefit more from new platform adopters when they are not part of a franchise (z = 1.7414, p < .10). While sensation-rich and/or familiar incumbents are not protected more from the direct competitive effects (Path A), these same incumbents benefit indirectly from software entry as they attract more interest from new platform adopters.GraphTable 5. Path C: Impact of New Platform Adopters on Incumbent Software Sales. 10 *p < .1.11 **p < .05.12 ***p < .01.13 Notes: SEs robust to platform clustering are in parentheses. Net overall impact (direct + indirect)We present the calculations for the overall net impact for every incumbent/new entrant combination in Table 6, Panel A, with a summary in Table 6 Panel B. Next, we walk through an example of how Table 6, Panel A, is calculated.GraphTable 6. Combining Paths A, B, and C. 14 Notes: SEs in parentheses given by the linear and nonlinear restrictions embodied in the coefficient calculation ([19]). Cannibalization = negative and significant net impact; Net neutral = insignificant net impact; Halo = positive and significant net impact.First, we calculate the total indirect impact (Paths B and C) by multiplying the elasticity of platform adoption to software entry (Path B in Table 4) with the relevant elasticity of incumbent sales to new platform adopters (Path C in Table 5). Consider the indirect impact of a superstar-franchise entrant on a franchise incumbent. For Path B, a 1% increase in superstar-franchise entry increases platform adoption by 4.9904% (see previous explanation); for Path C, a 1% increase in new platform adopters increases incumbent franchise software sales by.00676% (=.00242 + .00434 from Table 5, p < .01). Combining both, the full indirect impact of a 1% increase in superstar-franchise entry on incumbent franchise software sales is.0337% ( = 4.9904 ×.00676, p < .01).Second, the direct impact is calculated by combining the relevant coefficients from the heterogeneous coefficient estimations. For example, the direct impact of standard software entry on franchise incumbents is −.00969% (= −.01103 + .00134 from Table A2, column 1, p < .01); this is moderated by −.00335% if the entrants are superstar-franchise software (= −.00575 + .00240 from Table A2, column 4, p < .01). Totaling, the direct impact of a 1% increase in superstar-franchise entry on incumbent franchise software is −.0130% (≈ −.00969 + −.00335, p < .05).Third, we obtain the net overall impact by combining the direct and indirect impacts, which, for our example, equates to.0207% (=.0337 + −.0130, p < .10), a significant overall halo effect. For incumbent franchise software facing new superstar-franchise entrants, the indirect impact of greater sales from increased platform adoption outweighs the direct competitive effect.Table 6 performs these calculations and displays results for each type of entrant on each type of incumbent, including higher-order interactions (e.g., superstar-franchise new entrant on a franchise incumbent). As firms' incumbent-software portfolios differ, Table 6 enables managers to estimate the direct, indirect, and total impact new entrants have on their own portfolio.Importantly, we find significant net cannibalization of incumbents by new entrants without superstar status. The negative direct competitive effect of standard and franchise entrants (via Path A) outweighs any indirect effect (via Paths B and C). Incumbent franchise or superstar status does not offer protection from cannibalization due to competition with new nonsuperstars.In contrast, the impact of superstar and superstar-franchise entrants on new platform adopters is large enough to meaningfully counteract their direct competitive effects. The impact of new superstar-franchise software results in a net halo effect for superstar, franchise, and superstar-franchise incumbents and a net neutral effect for standard incumbents. Further, while new superstars do not produce any instances of a net halo effect, the indirect impact completely offsets losses due to direct competition for all but standard incumbents. Long-term effectsSo far, we analyze effects of entry in the current period. An advantage of dynamic models is they allow us to see how a current change in a variable impacts long-term results by working through the lags on the right-hand side. We show the long-term net overall impact of all entrant types on all incumbents in the Web Appendix, Table W7. Results are similar to those in Table 6; however, effect sizes are larger. The impact of new software entry on incumbents persists long after the introduction date, and the cumulative effect is large. Robustness checksWe run robustness checks for the Equation 3 results by including an indicator variable for exclusive software, dropping genre fixed effects, and dropping platform fixed effects. We also drop software/platform observations from the counts of available software if the observation is more than three years old ([26]) and estimate Equations 1–3. We detail these checks in the Web Appendix; results are similar to those presented previously. We also check single equation estimations to ensure that results are not driven by improved efficiency of joint estimation. The sign and significance level of all coefficients are the same. Discussion and ImplicationsBy observing how thousands of new software entrants affect the sales of incumbent software, we find evidence of both cannibalization and halo effects, depending on the software attributes. We also establish specific effect sizes for the direct and indirect impacts using large-scale data. Implications for Theory and ResearchResearch in the domains of new product development and platform markets can use our findings. First, with regard to Path A, prior network market studies rarely consider the direct relationship of new software entrants with incumbent software. A common assumption is that more software stock increases the platform's installed base, which increases software sales. However, we highlight the need to incorporate competitive cannibalization effects; otherwise, the results likely overestimate the impact of new software. We also reveal contingencies that alter whether new entrants help or hurt incumbent software sales, drawing from the sensations–familiarity framework. Software characteristics moderate the effects; by applying the superstar and franchise variables in novel ways, we show that they help quantify a new entrant's impact.Second, pertaining to Path B, which has been studied in various ways in the network effects literature, our findings move beyond investigating the impact of overall software stock on platform installed base, because we treat new software entrants as new products with unique impacts on market dynamics, not simply as increases in software stock. Furthermore, by accounting for new platform adopters separately from the past installed base of consumers, we show that new software entrants have different roles for facilitating new platform adoption.Third, pertaining to Path C, we quantify the impact of new platform adopters on sales of incumbent software, above and beyond purchases by the platform's existing users. Table A1 shows that the impact of platform installed base on incumbent software sales is positive (β = .4359, p < .01). However, it is not comparable to the impact of new platform adopters on incumbent sales shown in Table 5, as a 1% increase in new adoptions represents a very different number of consumers than a 1% increase in platform installed base. We observe 168,758 new adopters on average in a month; the average platform installed base in a month is 18,261,152. A 1% increase in platform adoption is equivalent to a.009% (≈ 1,688/18,261,152 × 100) increase in platform installed base, resulting in a.0039% (≈.009 ×.4359) increase in incumbent software sales. Compared with results in Table 5, incumbent superstar (β = .0352, p < .01), franchise (β = .0028, p < .01) and superstar-franchise (β = .0138, p < .01) software benefits more from new adopters than an equivalent increase in platform installed base; standard incumbents (β = −.0015, p > .10) benefit less, though the difference is not statistically significant.[ 8]Further, by accounting for new adopters attracted by new software entrants, we can test the totality of the indirect impact through platform adoption (Paths B and C). To the best of our knowledge, no study has examined and quantified this entire indirect path. Conventional wisdom suggests that new entrants help incumbents by increasing the platform's installed base, but we clarify that only new entrants with superstar status can drive platform sales so much that they result in a net positive impact (after accounting for direct cannibalization) on incumbent sales.Fourth, we offer an extension to the sensations–familiarity framework. This theory highlights the need for a balance between sensations and familiarity, without specifying their interaction. By showing that a measure of sensations (i.e., superstar) interacts with a familiarity variable (i.e., franchise), we establish that researchers should account for the additional effects that can be created by combinations of sensations and familiarity, beyond their independent direct effects.Fifth, we offer a new approach that accounts for the autoregressive process for platform and software demand. Using [ 4] random coefficients model and fixed effects to address heterogeneity in our panel, we extend that approach to incorporate a system-of-equations estimation while also dealing with endogeneity. We thus allow for heterogeneity in the impacts of new software entrants on incumbent software sales. Finally, by applying FGLS, rather than the less efficient ordinary least squares, we discern software-specific factors associated with halo and cannibalization effects. The Web Appendix has a step-by-step guide to implement this approach. Implications for ManagersOur study shows managers how to measure new product performance holistically. Among existing approaches, there have been ""few attempts to provide measures to quantify the effects of new products on the current product portfolio"" ([46], p. 359). Practitioners often adopt ad hoc estimates of cannibalization (e.g., [ 7]; [34]). Without historical data, managers might turn to A/B testing, but that approach is expensive and also requires clean test manipulation. With our method, leveraging the elasticities in Table 6, managers can estimate the net revenue of a new entrant by including both halo and cannibalization effects, along with the new entrant's own revenue.Here is a hypothetical, numerical example in which we calculate the impact of a new superstar-franchise entrant in the current month. The median number of superstar-franchise entrants in a month is zero; we consider an increase to one. Recall that we add 1 to the entry variables before taking the natural log; thus, one superstar-franchise entrant is a 100% increase in median superstar-franchise entry given the variable transformations (i.e., going from one to two instead of zero to one). Because a 1% increase in superstar-franchise entrants leads to a.0207% increase in franchise incumbent sales (Table 6), a 100% increase in superstar-franchise entrants leads to a 2.07% ( = 100 ×.0207) increase. Rounding from the descriptives in Tables W2 and W3, an incumbent franchise game earns ∼$179,000 per month, on average, and there are ∼235 franchise games on the market per month. Thus, the superstar-franchise entrant causes an additional $870,000 (≈ $179,000 × 2.07% × 235) in revenue for incumbent franchise games. We repeat this exercise for superstar and superstar-franchise incumbents because they also experience a significant impact from superstar-franchise entry (Table 6). The total financial impact on superstar, franchise, and superstar-franchise incumbents is ∼$1.3 million additional revenue in the month of introduction. Compared with the average of $5.9 million that new superstar-franchise software earns in its first month, the halo effect of a superstar-franchise entrant is 22% (≈ 1.3/5.9) of its own revenue. Thus, measuring only the superstar-franchise entrant's earnings underestimates the total impact.Consider a similar exercise with a standard entrant. The median number of standard entrants is two. Going from two to three is a 33% increase given the variable transformations. Table 6 shows standard entrants have significant cannibalization effects; one standard entrant equates to a loss of roughly $224,000 in revenue from all incumbents. The average revenue earned by a standard entrant in its first month is roughly $525,000; this suggests that 43% (≈ 224/525) of the revenue earned is simply from cannibalizing incumbents. A manager of a standard entrant might overestimate profitability if considering only revenue from the entrant's own sales.Moreover, because halo and cannibalizations effects are contextual, we show how to assess new entrants on a case-by-case basis, while accounting for specific characteristics of both the new product and the incumbent product portfolio. Managers of platforms or software firms can leverage our methods to assess the impact of a new product introduction; for managers of firms that provide platforms and software, we show how to estimate the holistic effect on revenues earned from sales of both platforms and incumbent software. Software-only firms can predict the impacts of their own new entrants and competitor entrants on their existing portfolio sales too.Finally, platform growth strategists can look to the introduction of the right type of new software products to spur demand. Thus, it is not sufficient to recommend only the traditional viewpoints of increasing the installed base (direct network effects) or software stock (indirect network effects); instead, managers should encourage strategic introductions of the right types of software. To design such strategies, managers can apply our method to gain a holistic view of the likely performance effects of new software introductions, based on their portfolio makeup. Limitations and Further ResearchLimitations of our study suggest further research. First, we study one industry; studies should build on and confirm our findings' applicability in other contexts. The video game industry is similar to other platform industries, but our theory and findings most likely generalize to other settings in which customers ( 1) might favor one platform but use several platforms, ( 2) view specific software products as imperfect substitutes, ( 3) consume multiple software products that address similar needs (often hedonic vs. functional), ( 4) repeatedly consume favorite software products, and ( 5) eventually become satiated and seek variety. Accordingly, we anticipate that our findings might apply to markets for streaming platforms, apply somewhat to markets for ride-sharing platforms/drivers, but apply less so to markets such as health insurance and hospital networks.Second, our econometric approach does not account for psychological mechanisms that underlie consumer behaviors in response to new entrants. The lack of process measures also is a common limitation of studies that use secondary data ([ 2]). Our theory development aligns with extant literature regarding why the observed effects occur, but additional research could test the psychological underpinnings of the behaviors we observe.Third, we evaluate some important moderators in platform markets; further research could evaluate others. Studies might address other operationalizations of superstar software. Our method dichotomizes quality, but examining quality on a continuum could yield new insights. " 27,Households Under Economic Change: How Micro- and Macroeconomic Conditions Shape Grocery Shopping Behavior," Economic conditions may significantly affect households' shopping behavior and, by extension, retailers' and manufacturers' firm performance. By explicitly distinguishing between two basic types of economic conditions—micro conditions, in terms of households' personal income, and macro conditions, in terms of the business cycle—this study analyzes how households adjust their grocery shopping behavior. The authors observe more than 5,000 households over eight years and analyze shopping outcomes in terms of what, where, and how much they shop and spend. Results show that micro and macro conditions substantially influence shopping outcomes, but in very different ways. Microeconomic changes lead households to adjust primarily their overall purchase volume—that is, after losing income, households buy fewer products and spend less in total. In contrast, macroeconomic changes cause pronounced structural shifts in households' shopping basket allocation and spending behavior. Specifically, during contractions, households shift purchases toward private labels while also buying and consequently spending more than during expansions. During expansions, however, households increasingly purchase national brands but keep their total spending constant. The authors discuss psychological and sociological mechanisms that can explain the differential effects of micro and macro conditions on shopping behavior and develop important diagnostic and normative implications for retailers and manufacturers.","Households are subjected to constantly changing economic conditions. These changes may take place at a personal, microeconomic level, such as if the main breadwinner receives a pay raise or a household member loses a job (micro conditions). Alternatively, changes may manifest at a macroeconomic level, in terms of the business cycle, with its recurring expansions and contractions or in response to global events such as the Great Recession or the COVID-19 pandemic (macro conditions). These changing micro and macro conditions substantially affect household spending and, in turn, companies' profits. By one estimate, the Great Recession led to an average 8%, or $4,000, decrease in real annual spending among U.S. households, which amounts to $500 billion in forgone revenues ([20]).While households tend to simply postpone purchases of durable goods to times of economic prosperity ([16]; [18]), they engage in a variety of adjustments when shopping consumer packaged goods (CPGs): switching from national brands (NBs) to cheaper brands or private labels (PLs), from supermarkets to discounters, from regular to promotional prices, or decreasing the amounts purchased altogether (e.g., [17]; [43]; [46]).While research to date has focused intensively on how households adjust individual CPG shopping outcomes in response to changing macro conditions (e.g., [17]; [41]; [43]), this work takes a holistic view on households' CPG shopping behavior by uncovering how it is differentially affected by micro and macro conditions. This explicit distinction is important because changes in macro and micro conditions are not necessarily aligned. In fact, even the Great Recession, during which unemployment rates skyrocketed and housing prices and stock portfolios plummeted, did not equally affect the personal income and wealth of all demographic subgroups of the population ([37]) or all geographical regions ([17]). Similarly, the economic downturn caused by the COVID-19 pandemic implies particularly severe microeconomic consequences for industry sectors that depend on tourism, events, or gastronomy, with less effect on banking or the public sector ([48]). Of course, an income loss, for example, as result of sudden unemployment, may as well occur during prosperous economic times and be no lesser of an individual hardship.Furthermore, the consequences of changing micro and macro conditions differ considerably. Whereas changing micro conditions directly affect households' ability to purchase, changing macro conditions, all else being equal, affect only households' willingness to purchase ([39]). Accordingly, households' response to changing conditions depends on whether they are affected at a micro or macro level (or both) and may manifest in very different shopping outcomes. For example, households may alter what they purchase (e.g., NBs or PLs) and where they shop (e.g., in discounters or supermarkets), as well as how much they spend and purchase. Thus, to properly disentangle the distinct effects of micro and macro conditions and to provide differentiated implications for retailers and manufacturers, holistic observations of households' shopping behavior are crucial.We analyze a total of seven measurable and managerially relevant shopping outcomes. These outcomes reflect how households allocate their budget across brand types and store formats—their shopping basket allocation (in terms of PL and NB spending in discounters and nondiscounters)—as well as how much they spend and purchase—their shopping basket value (in terms of total spending, purchase volume, and an index of prices paid). Through the analysis, we uncover and characterize the differential effects of micro and macro conditions on households' shopping behavior by addressing the following research questions: To what extent do micro (i.e., income) and macro (i.e., the business cycle) conditions affect households' CPG shopping behavior? How do micro and macro conditions differ in terms of their effects on households' shopping basket allocation and shopping basket value? Do asymmetries exist between negative (i.e., income losses/economic contractions) and positive (i.e., income gains/economic expansions) conditions, and if so, do these asymmetries differ between micro and macro conditions?We use a unique, comprehensive data set tailored to the research objectives. Drawing on the GfK Germany ConsumerScan panel, we obtain detailed information about daily CPG transactions for more than 5,000 households in Germany over a period of eight years including the Great Recession. Drawing on this, we identify what and where households shop, how much they purchase, what prices they pay, and how much they spend. Annual surveys administered to the panel provide us with longitudinal data on households' demographics and psychographics, including micro conditions in terms of household income. In addition, the panel data enable us to control for important marketing-mix elements concerning prices, assortments, and promotional activities. We further enrich the data set with macroeconomic data from the German Federal Statistical Office and advertising data from the Nielsen Company on advertising spending by all manufacturers and retailers in the sample.The analyses show that micro and macro conditions both have a substantial impact on households' shopping behavior. Importantly, households adjust their shopping behavior without a concrete change in their budget constraints. In addition, micro and macro conditions differ substantially in their effects on households' shopping behavior. Whereas micro conditions primarily have an impact on households' basket value, macro conditions not only affect households' basket value but also cause shifts in households' basket allocation. During adverse micro conditions, households buy lower volumes and spend substantially less in total but do not shift spending to other brands or store formats. In contrast, as macro conditions change, households shift spending to PLs (from both discounters and nondiscounters) during contractions and to NBs during expansions. In addition, they increase their total spending and purchase volume during contractions. We argue that the shifts during macro conditions are driven by a greater society-wide acceptance of frugal consumption that does not emerge during changing micro conditions. These discrete effects of micro and macro conditions and the proposed underlying mechanisms have distinct managerial implications. The results also address some of the counterintuitive findings of prior studies, such as increasing total spending and purchase volumes ([46]) as well as higher prices paid ([ 8]) during the Great Recession. Related LiteratureOur study relates to business cycle research in marketing as summarized in Table 1.GraphTable 1. Literature Overview. 1 Notes:[26] and [46] argue that changes in gasoline prices reflect changes in household budgets. We regard gasoline prices as macro effects because they are experienced simultaneously but not necessarily equally by all households, as some households may rely on their cars more than others. As such, they are more similar to macro rather than micro events.Pioneering studies in this stream show that during recessions, PL market shares ([43]) and discounter market shares ([41]) increase, and some of these effects carry over into subsequent expansion periods. [17] generally confirm these findings by analyzing PL demand at a household level, accounting for heterogeneous income and wealth effects caused by the Great Recession. They find significant short- and long-term effects on PL demand, albeit with notably smaller elasticities. [ 8] further extend the number of shopping behaviors observed. They find that unemployment caused by the Great Recession has led households to increasingly purchase products on price promotion, cheaper brands, and in cheaper store formats. Instead of traditional macroeconomic indicators, [26] and [46] use gasoline prices to operationalize changing economic conditions. They show that gasoline prices relate to a multitude of shopping behaviors such as spending, prices paid, and store format and brand type shares.In addition to macro conditions, some of the studies in the field observe households' micro conditions. However, they are either used as time-invariant demographic control variables ([ 8]; [26]; [46]) or conceptualized as direct consequences and part of macro conditions rather than distinct conditions with idiosyncratic effects ([17]). Our study thus contributes to this literature stream by delineating the distinct effects of changing micro as well as macro conditions on households' shopping behavior. Importantly, we also account for different magnitudes and asymmetries between adverse and beneficial micro and macro conditions.First insights into the differences between micro and macro conditions show that overall household spending on food products and alcoholic beverages increases during adverse macro conditions but decreases when micro conditions worsen ([38]). We complement these findings by analyzing a variety of shopping outcomes beyond overall spending, using actual purchase data (thus increasing external validity), and controlling for a large variety of confounding factors such as changes in the marketing mix that are associated with changes in macro conditions ([58]).Notably, studies to date either focus on individual shopping outcomes (e.g., [17]; [43]) or model several shopping outcomes independently from each other ([ 8]; [26]; [46]). However, households have a variety of means to adjust their shopping behavior that are also highly interdependent—for example, discounters carry substantially more PLs and fewer NBs and usually feature fewer promotions in favor of an everyday low-price strategy. As such, when households switch store formats, it almost automatically also affects their brand type and promotion shares ([11]). Failing to account for these interdependencies can overestimate the effect of changing conditions on individual shopping outcomes. Therefore, we analyze multiple shopping outcomes simultaneously, controlling for their interdependencies, and thus contribute to the literature by offering a holistic picture of micro and macro conditions' effects on households' shopping behavior. Conceptual FrameworkThe conceptual framework (Figure 1) depicts the two main components of our study: micro and macro conditions and their effect on households' shopping behavior. We observe these behaviors through concrete and measurable shopping outcomes that, in essence, boil down to households' shopping basket value (i.e., how much households purchase and at what price) and their shopping basket allocation (i.e., how households allocate their expenditures across brand types and store formats). To get a holistic picture of micro and macro conditions' effects on households' shopping behavior, we consider the various shopping outcomes simultaneously. We also control for household demographics and psychographics as well as manufacturer and retailer adjustments to the marketing mix.Graph: Figure 1. Conceptual framework. Economic Conditions: Micro Versus MacroWe analyze changing macro conditions in terms of the business cycle on the basis of gross domestic product (GDP) (e.g., [43]; [58]) and derive micro conditions in terms of households' income. Although changing macro conditions are experienced by an entire region, by a nation, or even globally, they do not necessarily affect all households at a micro level. For example, not all households may experience income reductions, job loss, or shrinking wealth during a recession ([17]). Thus, by differentiating between micro and macro conditions, we isolate the distinct effects on shopping outcomes of changes in households' ability to purchase (micro level) and their willingness to purchase (macro level) ([39]). A negative micro shock, for example, restricts some households' shopping budgets, while households that face only adverse macro conditions lack this budget constraint. Importantly, whereas changing micro conditions are usually a personal matter, changing macro conditions affect a society at large. Thus, shifts in macro conditions can alter what type of shopping behavior is considered the norm. During recessions, for example, frugal consumption such as buying PLs or visiting discounters may become socially acceptable and even fashionable ([22]; [38]).In addition, beneficial and adverse economic conditions exercise asymmetric effects on consumers' shopping behavior for several possible reasons, such as general pessimism following a recession, inertia in maintaining newly adopted habits, or the need to pay off debts that have accrued during a period of lower income ([11]; [43]). Thus, we investigate asymmetric effects by splitting micro and macro conditions into both adverse and beneficial changes. Households' Shopping OutcomesWe distinguish between a household's shopping basket value and shopping basket allocation. We examine shopping basket value outcomes in terms of a household's total budget spent, total volume purchased, and an index of prices paid that indicates whether a household purchases products below average market prices of these products, for example, through temporary price promotions. In this way, we can differentiate the degree to which households adjust how much they purchase and how much they spend. We discern shopping basket allocation outcomes by considering brand types and store formats jointly and differentiating between households' spending on ( 1) PLs in discounters, ( 2) PLs in nondiscounters (e.g., supermarkets, hypermarkets), ( 3) NBs in discounters, and ( 4) NBs in nondiscounters. Prior research has taken a similar approach to households' budget allocation, with studies distinguishing between PLs and NBs as different brand types (e.g., [ 3]; [63]; [56]) or discounters and nondiscounters as different store formats (e.g., [10]; [40]; [41]). This approach has the following conceptual merits. Brand typesRegarding brand types, PLs, NBs, and their competition have received ample attention from both academics and practitioners ([40]). PLs have evolved from pure economic options to covering all price tiers and even special segments such as organic foods ([27]; [35]). They have thus developed into major competitors for NBs; for example, in Germany they have gained a market share of 41%, with 95% of consumers buying PLs ([25]; [36]). The competition between NBs and PLs is distinct in that PLs are managed by retailers and, thus, they introduce an aspect of competition into their otherwise collaborative relationship with manufacturers through downward price pressure. However, at the same time, NBs and PLs benefit each other by increasing store traffic and reinforcing quality disparities ([24]; [49]). From a consumer perspective, NBs and PLs differ substantially. First, consumers perceive PLs as inexpensive and as a good value for money. Further, while NBs are generally still better known and are perceived as being of higher quality, PLs are catching up in terms of quality perception ([36]). These differences in terms of price and quality perceptions generally suggest that households will switch between these two brand types in response to changing micro or macro conditions. Thus, the explicit distinction between NBs and PLs is relevant for our research. Store formatsIn terms of store formats, previous research has contrasted discounters with ""traditional retailers"" ([40]; [41]), supermarkets ([10]), and large retail formats ([28]; [29]). In contrast to other formats, discounters are highly optimized for cost efficiency, resulting in a substantially different retail marketing mix: store design and product presentation are austere, consumer services are reduced to a minimum, and serviced fresh foods and baked goods counters are lacking. The assortment is typically limited, especially in terms of produce; shallow, with few alternatives in each product category; and dominated by PLs, featuring relatively few NBs. As such, discounters are able to offer substantially lower prices than other store formats at the cost of service quality ([41]; [64]).In contrast, the major nondiscount store formats, such as supermarkets, superstores, and hypermarkets, vary in floor size and assortments offered beyond CPGs (e.g., clothing, home decor, hardware) but are similar to each other in terms of prices, service quality, and CPG assortments ([40]; [41]; [64]). This is also evident from Table 2, in which we contrast market data from discount and nondiscount store formats in Germany. Therefore, distinguishing between discounters and nondiscounters is most obvious from both retailer and consumer perspectives. Despite their distinct characteristics, however, discounters and nondiscounters do not merely address different target groups but also compete directly with each other for the same consumers, as consistently argued and shown in previous research (e.g., [10]; [33]).GraphTable 2. Store Format Characteristics. 2 aSource:[21], based on 2016 data.3 bSource:[25], based on 2018 data.4 cSource:[16], based on 2018 data.5 Notes: Data are based on the German market. Aggregated values for nondiscounters based on sums or averages weighted by market shares. Service and price scores are indexes (0–100), scores for store formats are aggregates from the 12 major retail brands that were tested. We assigned retail brands to their primary store format based on industry convention and average store size: small retailers <400 m2, supermarkets 400–2,500 m2, superstores 2,500–5,000 m2, hypermarkets >5,000 m2 average sales area. Brand type and store format combinationsImportantly, we do not consider the defined brand types (NBs and PLs) and store formats (discounters and nondiscounters) in isolation but in combination. This combined view is important because the brand choice cannot be viewed independently of the underlying store format. For example, because discounters carry a larger PL share than nondiscounters, PLs are more visible to households at discounters and also compete with fewer NBs. At the same time, nondiscount formats usually offer more price tiers (e.g., economy, standard, and premium) and variants (e.g., organic, locally produced, or diet) for NBs as well as PLs within a product category than discounters ([27]; [35]). As such, PL and NB assortments differ structurally between discounters and nondiscounters, and we account for these differences by the combined consideration of these brand types (PLs and NBs) and store formats (discounter and nondiscounters). Thus, by crossing the two brand types and store formats, we obtain a parsimonious, mutually exclusive, collectively exhaustive, and meaningful conceptualization of households' shopping basket allocation. Altogether, the three shopping basket value outcomes and the four shopping basket allocation outcomes holistically cover the essence of households' CPG shopping behavior. Control VariablesWe control for household demographics, which play an important role in explaining differences in shopping baskets (e.g., [46]). In addition, we control for a set of household psychographics: price and quality consciousness, deal proneness, and out-of-home consumption preference. Psychographics control for household heterogeneity that is not necessarily captured by demographics because, for example, even households with high income may be deal-savvy or highly price-conscious ([ 2]). Such psychographics strongly resemble consumer traits that are largely stable in short-term environmental changes but also reflect long-term societal trends, cultural developments, and the process of consumer aging ([54]).As prior research has shown, retailers and manufacturers also react to macro conditions by adapting their marketing mix (e.g., [13]; [42]). We are less concerned with this relationship per se but control for adjustments in the marketing mix owing to their substantial influence on households' shopping behavior. Data Research ContextAs presented in Table 2, the German CPG retail market is split rather evenly between discounters and nondiscounters, with discounters accounting for 45% of revenues and 43% of stores.[ 6] Discounters in Germany are usually located in easily accessible and densely populated areas ([64]) and have an average sales area of 779 m2, which is slightly smaller than a typical supermarket (982 m2) and substantially smaller than superstores (3,461 m2) and hypermarkets ( 7,051 m_SP_2_sp_) ([21]). However, they carry far fewer stockkeeping units (SKUs) and offer a much larger PL share (65.6%) that typically outweighs NBs ([25]). Discounters' PL shares may vary by retailer (e.g., Aldi: 96%, Lidl: 61%), but even discounters with a relatively strong focus on NBs have a substantially larger PL share than nondiscounters (e.g., Penny: 42%, Netto Marken-Discount: 40% vs. nondiscounters: 21.2%). Discounters offer substantially lower prices but also limited service, as is evident from a study by the German Institute for Service Quality ([16]), which scores stores on the basis of their prices and service (higher scores mean better prices/service). The tested discounters received substantially higher (lower) price (service) scores than their nondiscounter counterparts. Discounters' focus on functionality rather than service is also reflected in their high space productivity (i.e., revenues per store space). Similarly, annual revenues per SKU are considerably higher in discounters (€30.4 million) than in nondiscounters (€3.2 million) ([21]).These data underline the similarity of the nondiscount store formats and their dissimilarity to discounters for the German market from both retailer and consumer perspectives, thus corroborating the previously introduced conceptual distinction between these two groups. Interestingly, this distinction is also reflected in the branding of different retail store formats in the German CPG market. For example, two major German retail companies—the REWE Group and the EDEKA Group—operate both regular supermarkets and superstores under their REWE and EDEKA umbrella brands. Their hypermarkets (REWE Center and E-Center) also incorporate many of the same brand cues. In contrast, their discounters—Penny and Netto Marken-Discount—carry retail brands that are completely distinct from their respective umbrella brand. Data SourcesTo reflect the particularities of the German CPG market, the data set draws on several sources and combines information across distinct aggregation levels. The primary data source is the ConsumerScan panel provided by GfK Germany, which includes transaction and survey data for panelists at the individual household level. As a major advantage, this panel covers private consumption comprehensively and representatively, spanning all German CPG retailers, including discounters that typically do not offer data for market research purposes through retail panels.[ 7] This data availability is particularly crucial, considering the substantial market share of discount stores in Germany (see Table 2). The panel also contains survey data for all panelists, based on self-reported annual demographic information (age, household size, and income) and psychographic measures (e.g., price and quality consciousness). In addition, we obtain data on weekly advertising spending that covers all major channels as well as all manufacturers and retailers from the Nielsen Company. Finally, we add publicly available GDP data from the Federal Statistical Office that indicate the aggregate economic condition. We thus build a unique, encompassing data set that combines behavioral measures with survey-based household demographics and psychographics, macroeconomic measures, and brand- and store-level advertising spending. Data PreparationThe initial raw data set from the ConsumerScan panel is composed of household characteristics and purchase decisions by 85,428 unique households—with 24,000 to 37,000 in any given year—that made more than 13 million shopping trips and 48 million purchases between 2006 and 2013. Purchase information is available at the SKU level for 39 product categories from 467 retailers, most of which maintain multiple stores. These products include alcoholic and nonalcoholic beverages (e.g., beer, fruit juice) and food (e.g., cereals, pasta, ice cream) as well as nonfood items (e.g., deodorants, detergents, toilet paper). For each purchased item, we have access to the unique product code, date and place of purchase, price paid, identifiers for store format, brand type, and temporary price reductions as well as specific product characteristics such as brand and manufacturer name and package size. In preparing these data, we took several cleaning and filtering steps at the purchase record and household levels. In particular, we eliminated inconsistent transaction records and households that did not remain in the panel for the entire period. This procedure is conservative and in line with prior literature (e.g., [17]). Data cleaning involved the following steps: ( 1) Removal of all cases with missing values, ( 2) removal of all cases with unusually high (more than four times the median) or unusually low (less than one-fourth the median) prices at the SKU level, ( 3) removal of all cases with SKUs purchased fewer than 25 times in the entire period.These data-cleaning steps preserved 97.4% of all observations and 96.1% of all expenditures. To exploit the analytical potential of panelists with long purchase histories and extensive survey information, we retain only households with at least one transaction per quarter (7,441 households) and full survey information from 2006 to 2013, leaving 5,101 unique households.To avoid structural differences between samples, we compared the filtered households with the remaining households in terms of shopping outcomes and demographics. Overall, we find only marginal deviations in purchase behaviors and demographic composition. Thus, we assume that the selected households with complete purchase histories are not structurally different from households with shorter or incomplete purchase histories. We also compare the filtered sample with information from the 2006 Microcensus ([15]). As in other studies using this type of data (e.g., [17]), our sample is only slightly older and has higher income, fewer single households and more two-person households, and fewer children. However, we find a sizable overlap in the distributions of the demographic variables, and we control for these demographics at the individual household level throughout the empirical analyses. Therefore, a lack of sample representativeness is not an issue. Detailed comparisons of the household samples are available in Web Appendix A. Variable Operationalization Shopping basket valueIn line with the conceptual framework, we consider multiple dependent variables to capture the two domains of shopping outcomes as exhaustively as possible. The first domain relates to a household's shopping basket value—that is, how much is spent by the focal household, as represented by three dependent variables. TotalSpendinght relates to the total CPG spending of household h at time t, measured in euros. PurchaseVolht refers to the total CPG purchase volume of household h at time t, again measured in euros. Note that a household's shopping basket typically contains products with different volume units (e.g., liters, grams, pieces) that cannot directly be combined into a total volume measure. Therefore, we follow [46] and use an average category price per volume unit from a one-year (here: 2006) initialization period and multiply it by the total equivalent volume units purchased in each category. This enables us to aggregate the purchase volume across categories. Accordingly, the resulting variable is expressed in euros. We note that any variations in this variable are caused by changes in volume and not changes in prices being paid that may result from switching between brand types and store formats. Therefore, we are able to clearly disentangle households' consumption (volume) from households' spending (value) of CPG purchases. Finally, PriceIndexht is constructed as an index ([ 1]) and compares, for household h at time t, the costs of the shopping basket at average market prices with the actual costs incurred by the household. These price differentials are considered for identical goods identified at the SKU level. As such, they do not reflect differences in the quality of goods purchased but whether specific SKUs in the basket were purchased at cheaper prices (e.g., through temporary price promotions). An index greater than 1 implies that a household paid more than average for the specific goods in its basket, and a value less than 1 implies that the household paid less than average. This variable, therefore, reflects households' cherry-picking behavior ([23]) and is not related to households' switching behavior between different brand types or price tiers. We provide further details on the construction of purchase volume and the price index in Web Appendix B. Shopping basket allocationThe second domain of shopping outcomes relates to a household's shopping basket allocation between combinations of brand types and store formats—that is, it captures how the household is allocating its budget. We measure this allocation with the dependent variable Spendingbht in terms of household h's total spending (in euros) at time t on the respective brand type–store format combination b: (b = 1) PLs in discounters (PLDisc), (b = 2) NBs in discounters (NBDisc), (b = 3) PLs in nondiscounters (PLNonDisc), and (b= 4) NBs in nondiscounters (NBNonDisc). Altogether, these four spending variables encompass each household's total spending.[ 8] Macro conditionsThe focal explanatory variables represent a household's individual micro conditions and the overall macro conditions. At the macro level, we first apply the Christiano–Fitzgerald random-walk filter ([ 9]) to the log-transformed quarterly GDP data to assess the general state of the economy itself. The extracted cyclical component of the GDP series constitutes the deviation from the economy's underlying long-term growth trend. Thus, periods with increases in the cyclical component indicate economic expansions, whereas periods with decreases indicate economic contractions. However, it is important to account for not only different phases of the business cycle but also the severity that comes with the depth of up- and downturns (e.g., [55]). To do so, we follow prior research ([43]; [58]) and define the magnitude of an expansion (contraction) period relative to the prior trough (peak) of the cyclical series, or the point in the cyclical component at which the quarter-on-quarter growth turns from negative to positive (from positive to negative). Therefore, we operationalize the symmetric measure of the business cycle (BCyclet) as changes in the cyclical component of GDP at time t relative to the prior peak or trough. In addition, to study potential asymmetries of macro conditions, we use the same operationalization to construct two semidummy variables that separately capture periods with an increase in the cyclical component relative to the prior trough as expansions (Expansiont) and periods with a decrease relative to the prior peak as contractions (Contractiont) of the economy. That is, Expansiont (Contractiont) takes values increasing (decreasing) with economic expansion (contraction) and 0 values during contractions (expansions).[ 9] Micro conditionsAt the individual level, micro conditions reflect a household's financial situation, captured by the household's monthly net income. The original income data included in the ConsumerScan panel are at a yearly aggregation level and are measured in 16 income brackets.[10] We construct a continuous income variable by taking midpoint values of these brackets in euros and transform the resulting series to a quarterly sequence (the aggregation level of the shopping outcome variables) by applying linear interpolation for each household.[11] We adjust income for inflation using the consumer price index. In line with the operationalization of macro conditions, we define micro conditions as a household's income change (IncomeChangeht) relative to its previous income peak or trough. This step enables us not only to capture income changes from one period to another but also to take the higher magnitude into account, which results from income changes along consecutive periods. Furthermore, we construct semidummy variables for positive (IncomeGainht) and negative (IncomeLossht) income changes that are equivalent to the operationalization of asymmetric measures at the macro level. Thus, IncomeGainht (IncomeLossht) is defined as the difference of the log-transformed net income at time t and the prior log-transformed income trough (peak), allowing us to account for the accumulated magnitude of income gains and losses over time. IncomeLossht and Contractiont are converted to positive values for ease of interpretation. Control variablesAs control variables, we include a household's value of the dependent variable from a one-year (here: 2006) initialization period t0 (TotalSpendinght0, PurchaseVolht0, PriceIndexht0, and Spendingbht0). In addition, we include demographics to control for household heterogeneity regarding household size (HhSizeht), age of the household head (Ageht), presence of children (Kidsht), and employment status (Unemployedht). We also include psychographic variables to control for heterogeneity in shopping-related traits and preferences in terms of quality (QualConsht) and price consciousness (PriceConsht), deal proneness (DealProneht), and preferences for eating out (EatOutht). While QualConsht and PriceConsht are based on fixed constructs provided by the GfK, we construct DealProneht and EatOutht from several survey questions. The associated items, factor loadings, and Cronbach's alphas appear in Web Appendix B, Table WB1. Demographic and psychographic controls are measured at an annual level, and we transform the psychographics to a quarterly series using linear interpolation.Finally, we include controls for the marketing mix. We compute this group of variables at different levels of aggregation as appropriate for each set of models and use household-specific product category weights to incorporate household heterogeneity ([46]). Except for the advertising measures, marketing-mix controls are based on transaction information from the ConsumerScan panel. Because we construct the marketing-mix controls on the basis of observed household transactions, we use only transaction information (e.g., prices, SKUs, price-promoted SKUs) of households that are not part of the analysis sample. Thus, we avoid potential biases resulting from nesting the transactions of these focal households into the marketing-mix controls. For the basket value models, we construct absolute measures for price (Priceht), assortment size (Assortht), price promotions (Promoht), PL share in assortments (PctPLht), and advertising spending of NBs (AdvNBt) and of store format j (with j = 1 for discounters and j = 2 for nondiscounters) (AdvStorejt), which includes advertising spending on retailer brands as well as their PLs. For the basket allocation models, the marketing-mix variables for each brand type–store format combination are computed relative to the average across all brand type–store format alternatives. Thereby, we parsimoniously account for potential cross-effects. In particular, we construct relative measures for price (RelPricebht), assortment size (RelAssortbht), price promotions (RelPromobht), PL share in assortments (RelPctPLjht), and advertising spending at the store level (RelAdvStorejt). Because advertising spending at the brand level refers to NBs only, we use it as an absolute measure.We adjust all spending and price variables for inflation using the consumer price index and advertising spending using the GDP deflator. Table 3 presents an overview of all variables and their operationalization, while Web Appendix B shows the detailed construction of the marketing-mix variables. Tables 4 and 5 provide the descriptives and correlations for variables in the shopping basket value models and shopping basket allocation models, respectively. Note the small correlations between micro and macro conditions, in support of the conceptualization of differential effects.GraphTable 3. Variable Operationalization. 6 Notes: Items, factor loadings, and Cronbach's alphas for DealProne and EatOut are presented in Table WB1 of Web Appendix B.GraphTable 4. Descriptive Statistics and Correlation Matrix for Variables in the Shopping Basket Value Models. 7 Notes: Means and standard deviations are based on untransformed values, correlations are based on log-transformed variables except dummy variables. BCycle, Expansion, and Contraction are multiplied by 100 to be expressed in percentage deviations.GraphTable 5. Descriptive Statistics for Variables in the Shopping Basket Allocation Models. 8 Notes: PLDisc = private labels in discounters; NBDisc = national brands in nondiscounters; PLNonDisc = private labels in nondiscounters; NBNonDisc = national brands in nondiscounters. ModelWe define regression models for the individual shopping outcomes and estimate them jointly in a system of seemingly unrelated regressions. To control for unobserved household heterogeneity, we use a random intercept specification. The three shopping basket value equations for total spending, purchase volume, and price index, as well as the four basket allocation equations for spending across four brand type–store format combinations, are specified in log-log form (excluding the dummy variables Kidsht, Unemployedht, and Quarterqt). This approach allows for an interpretation of coefficients as elasticities and accounts for the fact that households vary substantially in magnitudes of the dependent variables ([46]).[12] We first assume symmetry in each model with regard to the focal micro- and macroeconomic measures, where MacroEcont = δ1BCyclet and MicroEconht = δ2IncomeChangeht. Subsequently, we introduce asymmetric effects, where MacroEcont = γ1Expansiont + γ2Contractiont and MicroEconht = γ3IncomeGainht + γ4IncomeLossht.We provide the specifications for the shopping basket value and shopping basket allocation models subsequently. Shopping basket value modelsThe three shopping basket value models are defined as follows: ln(BasketValueaht)=αha+MacroEcont+MicroEconht+α2aln(BasketValueaht0)+α3aln(Priceht)+α4aln(Assortht)+α5aln(Promoht)+α6aln(PctPLht)+α7aln(AdvStoret)+α8aln(AdvNBt)+α9aln(HhSizeht)+α10aln(Ageht)+α11aKidsht+α12aUnemployedht+α13aln(QualConsht)+α14aln(PriceConsht)+α15aln(DealProneht)+α16aln(EatOutht)+α17aln(Timet)+∑24κq−1aQuarterqt+∑kωkaCopulakht+εaht, Graph( 1)where BasketValueaht is (a = 1) TotalSpendinght, (a = 2) PurchaseVolht, (a = 3) PriceIndexht, αha=α0a+μha,μha∼N(0,σμ2) , k is marketing mix variable k (k = 1, ..., K), q is quarter q in a given year (q = 1, ..., 4), and t is time period t at a quarterly level (t = 1, ..., T).We control for potential endogeneity in the marketing-mix variables resulting from unobserved shocks by including Gaussian copulas ([47]), which directly model the joint distribution of the potentially endogenous regressor and the error term through control function terms. An advantage of this method is that it does not require instrumental variables that may, as in our case given the number of marketing-mix variables across brand type–store format combinations, be difficult to find ([50]). A requirement is that the endogenous regressor is not normally distributed. Anderson–Darling tests and Kolmogorov–Smirnov tests confirm this nonnormality for all marketing-mix variables at p < .001. Given the large size of the sample, we also visually inspect quantile–quantile plots, which confirm nonnormality for all marketing-mix variables. The Gaussian copula for each marketing mix variable Xht for household h at time t is Copulaht = Φ-1[H(Xht)], where Φ-1 is the inverse distribution function of the standard normal and H(·) is the empirical cumulative distribution function of Xht. Shopping basket allocation modelsWe define the four models as follows: ln(Spendingbht)=βhb+MacroEcont+MicroEconht+β2bln(Spendingbht0)+β3bln(RelPricebht)+β4bln(RelAssortbht)+β5bln(RelPromobht)+β6bln(RelPctPLjht)+β7bln(RelAdvStorejt)+β8bln(AdvNBt)+β9bln(HhSizeht)+β10bln(Ageht)+β11bKidsht+β12bUnemployedht+β13bln(QualConsht)+β14bln(PriceConsht)+β15bln(DealProneht)+β16bln(EatOutht)+β17bln(Timet)+∑24κq−1bQuarterqt+∑kωkbCopulakbht+β18bInvMillsbht+εbht, Graph( 2)where βhb=β0b+μhb,μhb∼N(0,σμ2) , and the subscripts are as defined before.One issue with Equation 2 is that expenditures are zero where a household does not patronize a specific brand type–store format combination during a period. Considering only those observations with existing expenditures or adding a small constant may lead to biased estimates ([45]). This bias may be quite substantial in our case, where zero expenditures make up between 2.6% for NBs in nondiscounters and 20.8% for NBs in discounters of all the observations. To solve this issue appropriately, we follow the procedure for Type II Tobit models ([63], pp. 560–66). In a first step, we apply a probit model with a random intercept specification and pooled coefficients for brand type–store format choice. This approach allows for the fact that households may patronize multiple brand type–store format combinations. We use the same set of independent variables as in the basket allocation models and additional instrumental variables (average number of shopping trips and unique retailers visited, share of income spent on CPGs, and per capita CPG spending) for identification purposes. In a second step, we compute the inverse Mills ratio, InvMillsbht, based on the probit model results for each brand type-store format combination as InvMillsbht = φ(Xbht′η/Φ(Xbht′η), where φ is the standard normal density function, Φ is the standard normal cumulative distribution function, and η is the vector of parameters from the probit model. The inverse Mills ratio is then added for each brand type–store format combination as an additional independent variable in the basket allocation model to correct for interrelations between brand type–store format choice and spending. As before, we also add Gaussian copulas for all brand type–store format combination specific marketing-mix variables to account for potential endogeneity issues. Results Model Estimation and ValidationWe use Latent GOLD 5.1 ([59]) to estimate the seemingly unrelated regression system consisting of seven equations with a maximum likelihood approach. All the models converged before reaching the maximum number of iterations. Because we use data from 2006 for parts of the variable operationalization, we run the model on data from 2007–2013. For holdout validation, we randomly sample 500 households from the filtered data set and run the final estimations on the remaining 4,601 households. Starting with an intercept, time, and sample selection control model (Model 1), we sequentially add the dependent variable from the initialization period (Model 2); marketing-mix variables and endogeneity controls (Model 3); and demographic (Model 4), psychographic (Model 5), and symmetric micro and macro variables (Model 6). Finally, we replace the symmetric with the asymmetric micro and macro variables (Model 7). Table 6 provides an overview of the model-building process and fit statistics. Relying on the Akaike and Bayesian information criteria, Model 7 offers the best fit. We further scrutinize Model 7 for overfitting. We compare its mean squared errors and mean absolute errors between the estimation and holdout sample and find that they are very similar, showing no sign of potential overfitting.GraphTable 6. Model Building and Fit Statistics. 9 Notes: LL = log-likelihood; BIC = Bayesian information criterion; AIC = Akaike information criterion. Note that only models M3–M7 can be compared to one an other as they incorporate the same set of instruments and vary only by their exogenous variables ([19]). Symmetric Effects of Micro and Macro Conditions on Shopping OutcomesAlthough the asymmetric model (Model 7) shows the best fit, we briefly present the results from the symmetric model specification (Model 6) to check for internal consistency across the two models. Table 7 provides an overview of all significant elasticities of micro and macro conditions on basket value and basket allocation measures. The complete results of the symmetric model are available in Web Appendix C, Table WC1. Overall, we find significant influences on household shopping behavior for changes in households' micro and macro conditions. However, the nature of these influences clearly varies.GraphTable 7. Overview of Significant Elasticities. 10 *p < .1.11 **p < .05.12 ***p < .01.13 Notes: The table illustrates only significant elasticities. PLDisc = private labels in discounters; NBDisc = national brands in nondiscounters; PLNonDisc = private labels in nondiscounters; NBNonDisc = national brands in nondiscounters. Complete results of the asymmetric Model 7 are provided in Table 8. Complete results of the symmetric Model 6 are provided in Table WC1 of Web Appendix C. Micro conditionsIn line with economic theory, we find significant positive elasticities of income change on shopping basket value in terms of total spending (δ = .07, p < .01) and purchase volume (δ = .06, p < .01). Given that these elasticities are very similar in size and both variables are representations of a household's shopping basket in euros featuring comparable means, we can deduce that the majority of the expenditure effect is merely driven by volume adjustments. In fact, these volume adjustments are mainly attributable to purchases of NBs in nondiscounters, as indicated by the significant positive elasticity of income change on NB spending in nondiscounters (δ = .08, p < .01). Importantly, we do not find any structural shifts in households' basket allocation in that households increase (decrease) spending for a specific brand type–store format combination and simultaneously decrease (increase) spending for another. Macro conditionsUnder changing macro conditions, the results are different. We find marginally significant negative elasticities of the business cycle on shopping basket value dimensions (i.e., total spending [δ = −.06, p < .1], purchase volume [δ = −.06, p < .1], and price index [δ = −.01, p < .1]). Though intuitively surprising, the results confirm previous studies showing countercyclical CPG spending behavior of households (in value and volume) along the business cycle (e.g., [46]). In addition, we also find several significant elasticities of the business cycle on households' shopping basket allocation. In particular, the elasticity of the business cycle on PL spending in discounters (δ = −.70, p < .01) and nondiscounters (δ = −.63, p < .01) is significantly negative, respectively, whereas it is significantly positive on NB spending in nondiscounters (δ = .27, p < .01). This finding indicates that, to some degree, households shift from PLs in discounters and nondiscounters to NBs in nondiscounters—and vice versa—when macro conditions change. Moreover, when shifting their basket allocation across brand types–store format combinations, households also tend to purchase items at lower prices, for example, through temporary price promotions, as indicated by the negative effect of macro conditions on the price index. Asymmetric Effects of Micro and Macro Conditions on Shopping OutcomesTable 8 shows the estimation results of the asymmetric model. For better comparability of the impact of micro and macro conditions, Figure 2 provides an overview of the asymmetric effects of micro and macro conditions on basket allocation and basket value at their respective mean values—specifically, 2.42 (1.37) for Expansiont (Contractiont) and €176.70 (€124.77) for IncomeGainht (IncomeLossht), which translates to 7.8% (5.5%) of mean income. The findings from the symmetric model are confirmed by the asymmetric model, although the asymmetric estimation results show that the underlying effects are not symmetric but differ strongly in terms of size as well as significance between beneficial and adverse conditions.Graph: Figure 2. Asymmetric elasticities at mean values for micro and macro conditions.GraphTable 8. Results of Asymmetric Model 7. 14 *p < .1.15 **p < .05.16 ***p < .01.17 Notes: PLDisc = private labels in discounters; NBDisc = national brands in nondiscounters; PLNonDisc = private labels in nondiscounters; NBNonDisc = national brands in nondiscounters. Standard errors are in parentheses. Micro conditionsRegarding micro conditions, we again find that micro conditions primarily have an impact on households' shopping basket value but do not cause shifts in households' shopping basket allocation. However, the results reveal substantial asymmetries between beneficial and adverse micro conditions. Most notably, income gains have no effect on households' basket value or basket allocation; only income losses show significant effects. More precisely, a 1% loss in income decreases total spending and purchase volume by.12% (p < .01) and.11% (p < .01), respectively. Owing to the similar size of the elasticities, we can again assume that expenditure reductions are largely driven by volume reductions.[13] Given that income losses show no effect on households' price index, we can rule out the notion that expenditure reductions stem from households' shopping for lower prices.Importantly in the context of income losses, we also see no evidence that households shift their basket allocation to less expensive brand type–store format combinations. Rather, we find significant negative elasticities of income losses only on NB spending in nondiscounters (γ = −.16, p < .01) and PL spending in discounters (γ = −.10, p < .05), respectively. Thereby, we can conclude that the adjustments in purchase volume—and subsequently total spending—predominantly stem from abandoning NBs in nondiscounters and PLs in discounters when income losses occur. Instead of shifting to cheaper store formats, brand types, or both, households give up the relatively more expensive NBs in nondiscounters without substituting them with cheaper alternatives such as NBs in discounters or PLs in general. This lack of substitution is also true for PLs in discounters, but in this case options for shifting to even cheaper alternatives to reduce spending are limited, and therefore, volume adjustments are households' last resort. That is, households' primary means of coping with adverse micro conditions is to reduce expenditures on specific brand types and store formats and thereby reduce shopping basket value (i.e., spending less by purchasing lower volumes) rather than adjusting basket allocation by shifting to cheaper brand types or store formats. Macro conditionsIn contrast to adverse micro conditions (i.e., income losses), economic contractions not only have an impact on households' shopping basket value but also cause shifts in basket allocation. With regard to basket value, we find a significant increase in total spending and a marginally significant increase in purchase volume when the economy contracts: a 1% decrease in GDP, compared with its prior peak, increases total spending by.14% (p < .05) and purchase volume by.11% (p < .1). As already indicated for the symmetric model, previous studies also find countercyclical buying behavior of households during adverse macro conditions ([46]).[14] The results confirm and extend these findings by showing that increased total spending and purchase volume are not the only effects during economic downturns, as contractions also cause shifts of households' shopping basket allocation. In particular, we find significantly positive elasticities of contractions on PL spending in discounters (γ = .36, p < .05) and nondiscounters (γ = .51, p < .01), respectively; as well as a marginally significant negative elasticity of contractions on NB spending in discounters (γ = −.32, p < .1). These findings suggest that households shift from NBs to PLs during unfavorable macro conditions. Although previous studies find comparable changes (e.g., [17]; [43]), the combined results further illustrate one important phenomenon: even though households purchase PLs to a greater extent, they increase total spending and purchase volume. Moreover, the results suggest that by switching from NBs to PLs, NBs are not affected by economic downturns per se, but only in the context of discounters. That is, we only find the contraction elasticity of NB spending in discounters to be marginally significant and negative.The estimated elasticities during economic expansions further substantiate that changing macro conditions cause shifts in households' shopping basket allocation. Inversely to contractions, we find significant negative elasticities of expansions on PL spending in discounters (γ = −.94, p < .01) and nondiscounters (γ = −.71, p < .01), respectively. At the same time, we find a significant positive effect on NB spending in nondiscounters when the economy expands (γ=.52, p < .01). In addition, the results show a marginally significant and negative elasticity of an expansion on the price index (γ = −.01, p < .1). This result complements the findings on households' shifts from PLs in discounters and nondiscounters to NBs in nondiscounters during favorable economic times. In fact, to keep their purchase volume and total expenditures steady while shifting to more expensive NBs, households seem to actively seek price-promoted items to keep the prices they pay low.Overall, the results show major differences in the effects of micro and macro conditions on households' shopping behavior. While favorable micro conditions show no effect at all, adverse micro conditions lead households to reduce expenditures for specific brand types and store formats, resulting in lower total spending and purchase volumes. In contrast, favorable and unfavorable macro conditions primarily result in shifts of shopping basket allocation. These results highlight the importance of separating micro from macro conditions to identify their unique properties, effects, and implications. Effects of Control Variables on Shopping OutcomesAlthough the control variables included in the asymmetric Model 7 are not of primary interest, they are important to rule out rival explanations and thus to support the causal interpretability of the main results. Therefore, we briefly summarize them here; a more detailed discussion can be found in Web Appendix C. For the most part, when significant, the effects of the included control variables are intuitive and in line with prior research. Marketing-mix variablesAs expected, we find a marginally significant positive effect of assortment size (in terms of unique SKUs) on total spending and a significant positive effect on NB expenditures in nondiscounters. We also find several effects of promotion activity (in terms of unique SKUs sold on promotion): a negative effect on the price index, a marginally significant positive effect on NB spending in nondiscounters, a positive effect on PL spending in discounters, and a marginally significant positive effect on PL spending in nondiscounters. It is noteworthy that the effects for PLs are of smaller magnitude and confirm prior research showing that retail promotions are less positive for PLs than for NBs ([57]). We also find that the share of unique PL SKUs in the total SKU assortment has a negative effect on total spending and purchase volume, suggesting that focusing too strongly on PLs can have unfavorable consequences for retailers (e.g., [ 2]). Finally, advertising at the store level has the expected positive effect on total spending, purchase volume, and PL spending in nondiscounters, while NB advertising has an expected positive effect on NB spending in discounters.However, we also note that some of the effects are counterintuitive. This is particularly true for the negative effects of assortment size and PL share in assortments, negative own-advertising effects, and positive cross-advertising effects as well as the absence of significant price effects. Varying perceptions of PLs and NBs in assortments (e.g., [ 5]; [14]; [34]), underlying advertising spillover effects ([ 4]), or potential difficulties when measuring advertising effects ([51]; [52]) may provide reasonable explanations for these findings. Counterintuitive marketing-mix coefficients may, however, also be caused by the aggregation level of the data (quarterly, national-level aggregation across many individual brands, retailers, and product categories). Demographic variablesAs expected, we find that larger households tend to spend more across all four brand type–store format combinations, spend more in total, purchase larger volumes, and maintain a lower price index. Older households typically spend less on PLs in general as well as spend marginally significantly less on NBs in discounters, but more on NBs in nondiscounters while exhibiting a higher price index. Furthermore, the results suggest that households with children spend less on NBs in nondiscounters and marginally significantly less on NBs in discounters, respectively. Households that suffer from unemployment of the main breadwinner tend to spend less in total, corresponding to fewer expenditures on both NBs in nondiscounters and PLs in discounters. Psychographic variablesIn terms of psychographics, the analyses reveal many significant effects, generally underscoring the importance of accounting for such types of consumer characteristics ([ 2]). In particular, we find that quality-conscious households tend to spend more in total, more on NBs in nondiscounters, and less on PLs in nondiscounters. In comparison, price-conscious households typically spend more on PLs and less on NBs in general, spend less overall, and exhibit a lower price index. Deal-prone households, furthermore, spend more in total, purchase larger volumes, exhibit a lower price index, and spend less on PLs in nondiscounters, but significantly more on NBs in discounters and marginally significantly more in nondiscounters. Finally, households with preferences for eating out tend to spend less overall, purchase lower volumes, but exhibit a higher price index and typically show lower spending for PLs in discounters. Robustness ChecksWe perform several robustness checks to confirm the validity of the findings by applying alternative measures and indicators for micro and macro conditions. First, we use the growth rate of real GDP (e.g., [38]; [46]) and an index of consumer confidence (e.g., [ 3]) to assess the general state of the economy. To a large extent, the results are consistent in significance, direction, and magnitude with the main symmetric model (Model 6). Second, we use first-difference specifications of micro conditions rather than differences relative to prior income peaks and troughs as in the main asymmetric model (Model 7). All effects are consistent in significance and direction, even though the elasticities are of a higher order of magnitude. Third, we introduce an individual-level measure of a household's perceived financial situation into both main models. This measure captures changing perceptions of micro conditions that are not reflected in household income (e.g., wealth). Controlling for individual financial perceptions does not alter the findings regarding income, and we can confirm all effects to be consistent in terms of significance, direction, and the order of magnitude. All significant effects of the financial perception measure itself are in line with economic theory. We present and discuss these results in greater detail in Web Appendix C. DiscussionMicro and macro conditions have significant effects on households' shopping behavior and outcomes that, by extension, may affect firm performance of retailers and manufacturers. By observing shopping basket allocation across brand types and store formats as well as shopping basket value in terms of total spending, purchase volume, and an index of prices paid, this research provides an extensive analysis of how (through shopping basket allocation) and how much (through shopping basket value) households adjust the various facets of their CPG shopping behavior. Thus, we distinguish the effects caused by micro conditions in terms of income and macro conditions in terms of the business cycle. In addition, we account for possible asymmetries between adverse and beneficial conditions. These findings, based on a rigorous modeling approach and longitudinal field data, have important diagnostic and normative value for managers and contribute to previous research on business cycle effects. We provide an overview of the results and associated implications in Table 9.GraphTable 9. Overview of Results and Implications. The results uncover and juxtapose the specific effects of micro and macro conditions on shopping behavior. We find that both micro and macro conditions have pronounced effects on households' shopping behavior that are distinct from one another and asymmetric for positive versus negative conditions. Some findings are especially intriguing: micro conditions affect only households' overall consumption levels, whereas macro conditions also lead to structural shifts in households' budget allocation across brand types and store formats. In addition, during changing macro conditions, household adjust their shopping behavior even if they are not affected financially (as we control for income). In this section, we first summarize the results and subsequently discuss potential underlying psychological and sociological mechanisms before addressing interaction effects and asymmetries. Micro ConditionsAlthough no significant adjustments in shopping basket allocation or value emerge for income gains, income losses lead to a general decline in CPG expenditures. This drop is largely driven by households purchasing less and thus spending less. The overall decrease in consumption specifically affects PLs purchased in discounters and NBs purchased in nondiscounters. These findings show that, rather intuitively, budgetary constraints lead to decreased consumption, adding to extant research that has mostly taken a spending perspective (e.g., [38]). However, the absence of structural shifts in households' budget allocation is noteworthy. Theoretically, households could also reduce spending by switching to a cheaper store format or brand type, but instead they generate savings primarily through volume reductions. Macro ConditionsIn contrast, changing macro conditions evoke structural shifts in households' basket allocation. During contractions, we see expenditures for NBs purchased in discounters being reallocated to PLs purchased in discounters and nondiscounters. While this seems intuitive, it is interesting to note that this shift is accompanied by a general increase in total spending driven by households buying more. In other words, even though households switch to PLs during contractions, they end up spending more in total.During expansions, households reallocate their purchases from PLs (purchased in nondiscounters as well as discounters) to NBs purchased in nondiscounters. Interestingly, we also find that total spending and volumes purchased remain unaffected at the same time, because households focus more on getting deals, as indicated by a decline of the index for prices paid. As such, households switch to a more expensive brand type during expansions although their budget remains constant (as we control for income), which seems to be feasible as they increasingly purchase products on price promotion. Plausible Mechanisms Underlying Micro and Macro EffectsSeveral theoretical mechanisms can explain our findings. First, the findings suggest that adverse macro conditions may have a societal impact that trickles down to individual households even if they are not affected at a financial level. In trying times, frugal consumption, such as buying PLs or shopping at discounters, seems to become more socially acceptable and even fashionable ([22]; [38]), which is in line with the shifts of budgets toward PLs in (non)discounters that we observe during contractions. Just as much as frugal consumption may become increasingly commonplace during contractions, purchasing NBs may become a societal norm and is required if households want to maintain their social standing during expansions ([38]). In accordance with that norm, households seem to drop PLs in favor of NBs in nondiscounters even though they have no increase in budgets, as we see in the results. They seem to accommodate this shopping behavior by being price-savvy, shopping products on price promotion. Price promotions may also offer a welcome justification for households to abandon the PLs they have adopted during prior contractions in favor of NBs.This reasoning is also consistent with the lack of shifts in the face of adverse micro conditions, as described previously. An income loss, independent of macro conditions, is first a personal hardship rather than one shared by society. Therefore, there is not a general move to and acceptance of PLs and discounters, as in the case of adverse macro conditions ([22]; [38])—households do not switch to these cheaper brand types or store formats but instead reduce their overall consumption. In addition, income losses may weaken self-confidence and, thus, awaken a desire to bolster one's social status ([31]; [53]), which may lead households to continue buying NBs while economizing on volume to accommodate their lower income.Another explanation for these findings may lie in households' perception of the nature of micro and macro conditions. While a nationwide or global contraction is beyond households' direct control, personal income can be influenced through concrete actions. This discrepancy in the ""mutability"" of the conditions leads to different reactions in households: whereas high-mutability conditions (here: micro conditions) result in high self-regulation, planning, and prioritizing, low-mutability conditions (here: macro conditions) elicit a desire for restoration of control ([ 7]; [31]). Adverse micro conditions lead households to self-regulate by reducing their overall consumption, whereas adverse macro conditions result in a desire to restore control through actions that are perceived as more frugal (i.e., purchasing PLs). Control-restoration behaviors are also associated with compensatory consumption, such as in the form of overspending and higher food intake ([ 7]; [44]), which may explain the overall increase in household spending and which is potentially aggravated by the lack of a budgetary constraint that would limit this behavior ([62]).Other explanations of the increased consumption may lie in households' shift to PLs, which usually are associated with larger package sizes and lower product prices and which have been shown to increase consumption ([ 6]; [61]). Similarly, these factors contribute to households' purchase of increased quantities when shopping in warehouse club stores ([ 4]). In addition, adding discounter visits to a shopping trip may increase households' spending owing to self-licensing and self-control depletion ([31]). Asymmetries and InteractionsLike previous studies in the field, we find asymmetries between adverse and beneficial conditions for both micro and macro conditions. In the case of micro conditions, we find that income gains generally have no significant effects on shopping outcomes, whereas income losses do. This finding suggests that households are quick to decrease spending when income decreases but are slow to respond when income increases, potentially because they need to compensate for postponed purchases of durables or paying off debts ([11]). While contractions affect households' shopping basket value more extensively than expansions, the expansion elasticities for shopping basket allocation are mostly larger than during contractions. This response seems reasonable, as failing to keep up with one's surroundings during an expansion would translate into a loss of status, whereas not adopting a more frugal shopping behavior during a contraction implies an increase in status ([38]). In addition, we find more pronounced asymmetries between adverse and beneficial conditions at the macro level than at the micro level. Thus, adjustments in shopping behaviors may reverse more quickly when they are caused by changing micro conditions compared with macro conditions. Given that adverse macro conditions shift the societal acceptance of certain brand types and store formats, households' attitudes may change ([32]). This reasoning implies that macro conditions' effects on shopping outcomes linger longer than micro conditions, during which households engage in status-maintaining shopping behaviors. Therefore, these status-maintaining shopping behaviors may be a means to an end rather than an attitudinal shift and households would quickly discard them once conditions improve.Finally, we investigate whether micro and macro conditions and the underlying mechanisms that affect households' shopping behavior moderate each other. Thus, we perform a post hoc analysis to test for possible interaction effects for which we present complete results in Web Appendix C, Table WC3.[15] Interestingly, the main effects remain unchanged while all interaction effects are insignificant, which suggests that micro and macro conditions do not moderate each other. Thus, the results indicate that the effects and mechanisms that micro and macro conditions elicit occur independently from each other. That is, if both conditions change simultaneously, their individual effects on households' shopping outcomes work in parallel. Managerial Implications Micro ConditionsChanging micro conditions affect shopping outcomes only when households suffer income losses rather than gains, leading to a decrease in PLs purchased in discounters and NBs purchased in nondiscounters. To buffer the negative effects of when and where they expect wages to decrease, manufacturers as well as discounters can profit from listing NBs in discounters. In particular, hard discounters such as Aldi and Lidl, whose overwhelming majority of revenues stem from their own PLs, may profit from this strategy. Thus, we provide an additional perspective to the literature investigating the role of NBs in discounters (e.g., [12]). If households indeed suffer from weakened self-confidence and want to bolster their social status as a result of adverse micro conditions ([31]; [53]), NB manufacturers and nondiscounters may leverage this reaction by using status appeals in their advertising. Because adverse micro conditions lead to a general decline in consumption, retailers and manufacturers could target product categories that are affected the most with marketing-mix actions. Changing micro conditions may be especially hard for manufacturers and retailers to identify, but with increasing availability of data through loyalty cards and online shopping, managers could detect the specific shopping outcomes associated with these changes and address those households through personalized coupons and deals. Macro ConditionsChanging macro conditions substantially affect households' shopping basket allocation as well as value. Given the increased acceptance of PLs during contractions, retailers can use the opportunity to extend their PL portfolio into higher price tiers and product categories with high involvement and complexity ([56]). In addition, they may narrow their price gap to NBs and strengthen their branding to preemptively counteract households' shifts back to NBs during subsequent expansions. During expansions, they could then offer more attractive and profitable price promotions. In particular, nondiscounter PLs may get away with raising prices because they are unaffected by increasing budgetary constraints. Given the countercyclical susceptibility of PLs, retailers should adjust their assortment accordingly, reducing their PL share in expansions and increasing it in contractions. While hard discounters are especially susceptible to adverse micro conditions, soft discounters (i.e., discounters with a relatively low PL assortment share) should be aware of contractions owing to the substantial negative effect of NBs purchased in discounters and their comparatively low share of PLs that may compensate the losses.Because we control for micro conditions, the reallocation of budgets to PLs that we observe during adverse macro conditions is apparently not driven by monetary factors but instead may result from changing attitudes toward frugal consumption across society ([38]) and a desire to restore control ([ 7]). If this reasoning holds, it has important implications for managers. NBs and retailers can avoid costly price reductions that are ineffective given the lack of a more constrained budget and instead use measures that provide a perception of frugality.[16] These measures may allow households to engage in behaviors that they associate with economizing but, at the same time, are economical for the retailer or manufacturer. For example, loyalty programs can offer low price discounts and small rewards, giving households the perception that they engage in frugal consumption ([45]). Distribution of (digital) store fliers may create a sense of greater control over the planned shopping trip. In addition, communication may highlight the quality and reliability of products to reduce uncertainty and increase compensatory consumption. NB managers might also consider increasing package size, as larger package size is often associated with a lower per unit price ([ 6]). Finally, NB managers and retailers can leverage the higher cognitive load and depletion of self-control resulting from switching stores and/or brands ([60]), rendering shoppers more susceptible to in-store promotions ([31]). Limitations and Directions for Future ResearchWhen individual income is controlled for, changes in observed shopping behaviors resulting from macro conditions are clearly linked to households' willingness, rather than ability, to purchase. Potential underlying changes in attitudes and societal acceptance of certain shopping behaviors provide a conclusive basis for our argumentation. However, we do not observe these changes of attitudes in the data directly. Therefore, we encourage field experiments and laboratory studies to dive deeper into the underlying psychological and sociological mechanisms that might drive these findings. These insights can be crucial in predicting how households will change their CPG shopping in reaction to other types of macro conditions, such as a worldwide pandemic.Including demographics and psychographics, we control for household characteristics but do not account for heterogeneity in households' reaction to changing conditions, which should be addressed by future research. Heterogeneity may originate, for example, from households' differing preferences for high-quality products, with those preferring high quality potentially opting for adjustments in the volume purchased and the price paid for a good over switches to low-tier NBs and PLs. Alternatively, heterogeneity might stem from households' usual ""baseline"" shopping behavior because it influences whether and how they are able to economize during adverse conditions.Future analyses could also differentiate among different product categories, especially relating to the reduction in consumption levels caused by adverse micro conditions. Some product categories may be more essential than others and, thus, consumption may not simply be reduced ([38]). Some product categories may even experience increasing consumption—for example, as households shift from soft drinks and juices to plain water.Finally, previous research has shown that macro conditions affect marketing-mix decisions ([58]). Thus, future research could take a corporate rather than household perspective, investigating how managers detect and react to changes in micro conditions. " 28,How Consumer Orchestration Work Creates Value in the Sharing Economy," Sharing economy platforms have become increasingly popular, but many platforms do not create all the value that is possible because consumers face challenges while cocreating their experiences. The authors situate the origin of these challenges in the sharing economy's hybrid cocreation logics, which combine competing communal and transactional logics. Using a qualitative study of Couchsurfing, a platform for sharing free accommodation, the authors find that consumers engage in orchestration work to overcome cocreation roadblocks and extract greater benefits from sharing economy platforms. This orchestration work consists of many actions reflected in four overarching mechanisms: consumer-to-consumer alignment, rewiring relations, trust investment, and network experimentation. The authors connect these mechanisms to known sources of value for firms (i.e., complementarities, efficiency, lock-in, and novelty) to make recommendations for how platform firms can foster consumer orchestration work and unlock the full value of consumer cocreation in the sharing economy.","Sharing economy platforms are common and growing quickly across industries ([45]). These platforms work as digital marketplaces in which consumers (peer service providers and service users) act as cocreation partners to one another, and platforms facilitate this cocreation. While increasingly popular, many platforms may not be creating all the value that is possible because their consumers face competing institutional logics (i.e., guiding principles) that make cocreation challenging. For example, platform consumers need to cocreate experiences despite differing in terms of their goals and values (e.g., Airbnb homeowners and guests may have different understandings of what ""comfortable,"" ""clean,"" or ""convenient"" means). Likewise, platform consumers need to reconcile their desire for impersonal transactions and meaningful social interactions when cocreating experiences (e.g., an Uber driver and rider may differ in whether they prefer a quiet ride or a pleasant conversation). Platform consumers need to manage the risk of cocreating with strangers (e.g., Couchsurfing hosts and guests must assess whether to sleep next to a stranger). Further, platform consumers need to find ways to create personalized experiences while collaborating with others who may also want to create their own personalized experiences (e.g., TaskRabbit ""taskers"" need to figure out how to offer their skills while attending to the specific needs of those who ask for help). We argue that consumers try to solve these challenges by engaging in orchestration work, which we define as the set of actions that consumers engage in to overcome cocreation challenges.This article explains what platform firms can do to help consumers resolve these challenges and unlock the full value of the orchestration work that consumers are willing to undertake to acquire benefits from cocreating in the sharing economy. Whereas the platform firm could also be considered a cocreation partner, here we focus on how consumers cocreate among themselves, using platform affordances, which are the opportunities for action shaped by a platform's design features. To develop these insights, we conducted a qualitative study of Couchsurfing, a sharing economy platform launched in 2004, in which consumers, through sharing free accommodation, cocreate cultural experiences of hospitality. In some platforms, the roles of peer service provider and user are clearly separate (e.g., Uber's drivers and riders); in others, platform consumers perform both roles at the same time (e.g., Tinder's consumers). We use the term ""consumer"" to capture both roles given that both parties are actively participating in the platform; if only the provider or user role is creating value, we note this.This article makes four contributions to value cocreation research and the understanding of marketing in the sharing economy. First, it introduces the hybrid cocreation logics of the sharing economy. Considering these logics, we clearly identify and outline the challenges that platform consumers face when cocreating: the challenge of heterogeneity in cocreation, the challenge of networked sociality for cocreation, the challenge of interpersonal trust, and the challenge of personalizing cocreation.Second, this article introduces consumer orchestration work, offering a novel understanding of how platform consumers overcome cocreation roadblocks as they navigate the identified challenges. It also maps orchestration actions to associated mechanisms that constitute orchestration work: consumer-to-consumer (C2C) alignment, rewiring relations, trust investment, and network experimentation. In doing so, this article highlights the role of cocreation partners and platform affordances helpful in shaping these actions and mechanisms.Third, this article addresses a missing link between value cocreation benefits to consumers and platform firms by explaining how consumer orchestration work can lead to complementarities, efficiency, lock-in, and novelty ([56])—known sources of value creation for platform firms. Drawing on these findings, we offer actionable and practical recommendations to sharing economy platforms for leveraging consumer orchestration work in support of value creation.Finally, this article provides a series of research questions to spur more work on value cocreation in digital platforms, consumer experience in the sharing economy, and sharing economy governance and policy. Overall, we offer marketers a novel framework for understanding and managing value cocreation by consumers in the sharing economy. The Challenge of Cocreating Value in the Sharing Economy The Backdrop of Competing Institutional LogicsSharing economy platforms draw on multiple, and often competing, institutional logics that prescribe goals, norms, and identities and shape how actors feel, think, and act in that context ([47]). In particular, the contrast between communal and transactional logics ([45]) and the hybrid resulting from their interplay ([52]) creates specific conditions that guide cocreation in this context. Aligned with the understanding that institutions shape value cocreation in service ecosystems ([51]), we refer to the normative cocreation principles established by institutional logics as ""cocreation logics.""Communal cocreation logics refer to the principles that guide cocreation when community-oriented partners, who are motivated by shared values and goals, interact to perform shared social practices. With roots in the personal social interactions of Gemeinschaft ([48] [1887]), communal cocreation logics usually entail relationships based on mutuality ([ 3]) and high consociality, that is, high physical and/or virtual copresence of social actors in a network ([34]). In contrast, transactional cocreation logics refer to the principles that guide cocreation when self-interested actors, who have diverse values and goals, interact through formal, contractual, and socially distanced relations ([21]). With roots in the impersonal roles of Gesellschaft ([48] [1887]), transactional cocreation logics predominantly entail one-off quid pro quo exchanges that require money or another token ([39]).The interplay of these two logics produces the hybrid cocreation logic of the sharing economy, defined as the competing set of principles that combines communal and transactional logics to guide cocreation among consumers in the sharing economy. Mutuality may be present, but most interactions are one-off, quid pro quo exchanges between strangers guided by heterogeneous values and self-interested motivations ([16]). As such, shared practices that could work as normative structures ([51]) for organizing cocreation tend to not develop. Whereas platform firms may regulate the exchange of money and services through informal contracts, these are insufficient to organize the vastly varied cocreation interactions that happen in the sharing economy ([12]). As a result, consumers face specific challenges when cocreating experiences in the sharing economy. Four Challenges Created by the Hybrid Logics of the Sharing Economy Challenge of heterogeneity in cocreationA key challenge is that platform consumers are often very heterogeneous in terms of their resources, goals, and values. As a result, consumers may end up cocreating with partners who differ largely from them, which research has found can be destabilizing. Under a communal cocreation logic, consumers deal with the challenge of heterogeneity through frame alignment practices that ""facilitate the accommodation of differences, legitimize heterogeneity, and protect community continuity"" ([46], p. 1011). However, the communal approach implies that consumers are trying to find a common frame for cocreation, which is often not the case in the sharing economy ([12]). Challenge of networked socialityOnline platforms afford intense, highly consocial connections among consumers ([34]; [54]). However, because a hybrid logic is operating, these relations are more self-interested and transactional than those of communal sociality ([22]). As a result, consumers need to navigate hybrid relationships with cocreation partners who are situated between the close, identity-shaping relations of communities and the often impersonal, transactional relations of commercial settings. Although this is a ubiquitous challenge in relations mediated by digital platforms ([22]), it becomes much more crucial in hybrid cocreation logics in the sharing economy. Prior research shows that digital platform consumers navigate tensions that emerge from contrasting logics of sociality in the sharing economy ([52]). However, it remains unclear how consumers use platform affordances to establish relationships without relying on communal logics. Challenge of interpersonal trustSharing economy platforms are environments of distributed trust that are underscored by digital anonymity and/or ambiguity ([ 5]). Guided by hybrid logics, consumers cocreate experiences with strangers without assurance that informal regulation mechanisms, such as reputation ratings, are effective in reducing opportunism ([12]; [45]). This requires that consumers engage in ""trust leaps"" ([ 6]) as they cocreate with others on the platform. It is challenging, then, for consumers to assess the integrity and reliability of potential cocreation partners in ways that reduce the risk of cocreating experiences with strangers in one-off exchanges. One such risk is potential exploitation by cocreation partners, who may act on the basis of divergent values and norms. This challenge becomes even more pressing when cocreation involves intimate experiences that demand higher levels of interpersonal trust, such as sharing a home or going on a date. Prior research on the sharing economy (e.g., [29]) has highlighted that trust develops as a combination of relational and calculative aspects, and that cocreation partners should work to cultivate trust. However, it is unclear how such work unfolds. Challenge of personalizing cocreationGuided by hybrid cocreation logics, sharing economy consumers pursue personalization and continuously reconfigure resources to accommodate their shifting individual desires ([36]; [37]). Whereas cocreation partners often expect interactions to lead to personalized experiences, the cocreated nature of these experiences demands that personalization be sought through collaboration with others. This leads to personalization roadblocks. Prior research indicates that individuals strive to tailor their cocreated experiences to their specific interests through self-directed customization ([28]). However, it is unclear how consumers navigate personalization roadblocks during cocreation when their partners are guided by different values or have conflicting goals.These challenges become more pressing when direct marketing controls (e.g., service standards and quality checks) by the platform provider are difficult to implement (e.g., when experiences happen mostly offline or outside of the platform's control), or are not desirable (e.g., when nonstandardized experiences are part of the value proposition). Given these challenges to value cocreation in the sharing economy, we develop an empirically grounded framework of consumer orchestration work aimed at addressing these challenges. Our study is motivated by the following question: How do consumers navigate the challenges of cocreating experiences in the sharing economy, especially in the absence of direct platform firm control? Research ContextCouchsurfing is often considered ""the original sharing economy platform"" ([44], p. 38). Like other hospitality platforms (e.g., Airbnb, HomeAway), Couchsurfing connects hosts (i.e., platform consumers who are offering accommodation) with guests (i.e., platform consumers searching for a place to stay), and the term ""couchsurfer"" is often used to define all platform consumers independently of the role they take at each interaction. Like many consumer collectives in the sharing economy, exchanges and interactions in the Couchsurfing network are facilitated by an online platform that also keeps track of couchsurfers' reputations ([12]). Couchsurfing is nevertheless considered a purer, more authentic collective than other platforms, such as Uber and Airbnb, because this platform firm only minimally intervenes in how its 12 million consumers cocreate experiences ([41]). At its inception, Couchsurfing was largely guided by the principles of mutuality and generalized exchange ([ 3]), with consumers sharing free accommodation and collaborating to design and maintain this nonprofit platform. Since 2011, however, the platform has been managed by Couchsurfing International Inc., a for-profit organization that has actively pursued options, including a monthly/yearly charge, to capture the many forms of value that its consumers cocreate. Changes in the platform's configuration and its recruiting of consumers have led to the proliferation of quid pro quo exchanges on it ([12]) and the implementation of advertisements and freemium features, characterizing it as a hybrid economy ([39]). Similar to the way that some Airbnb consumers are guided by the logic of hospitality, treating their guests as friends ([52]), some couchsurfers subscribe to both communal and transactional logics, seeking friendships while keeping tabs on what they give and receive when cocreating through the platform.For the platform firm, the challenge in capturing value is that ""the actual time that the two (or more) Couchsurfing partners spend together [and which] constitutes the most important part of the Couchsurfing experience"" ([20], p. 510) happens offline, in intimate home settings, and is largely outside the platform firm's direct control. Thus, in contrast to other platforms in the sharing economy, where the platform firm exerts some control over the setting of the experience (e.g., Uber determines the characteristics of the cars in its fleet, Airbnb checks the features of the properties it lists), Couchsurfing presents an extreme case of how the orchestration of experiences unfolds when it is led by consumers. If seen from [34] framing, Couchsurfing offers consumers low platform intermediation and high consociality. Couchsurfing relies little on the standardization afforded by commercial mechanisms (e.g., prices are not available as a parameter for comparing potential hosts, the absence of set rules for check-in/out times requires negotiation between cocreation partners), and the platform's technical features are limited (e.g., guests cannot browse a map of hosts). Thus, experiences cocreated by couchsurfers are extremely heterogeneous, making consumer orchestration actions more necessary, frequent, and salient.Participants consider their Couchsurfing experience in terms of a stay[ 7] (including pretrip, on-trip, and posttrip expectations, interactions, and responses). Before traveling, a couchsurfer searches the platform for hosts to find accommodation or other couchsurfers to socialize with at the travel destination. Couchsurfers then select potential hosts and exchange messages on the platform or elsewhere (e.g., via social media or SMS) to get to know each other better and plan the stay. When a stay is confirmed, the host and guest continue to interact online before their first face-to-face meeting, which usually takes place in a public place. At home, hosts may choose to share their house keys with guests, cook for them, or include them in their household routines. Often, hosts act as local guides and give insider tips to guests, enabling them to experience a place from the perspective of a local. After a stay, the Couchsurfing platform invites guests and hosts to log their experiences on the platform by writing and posting references within 14 days. References are posted on a couchsurfer's profile and classified as would stay again or would not stay again. If a participant does not leave a reference, the stay is not registered on the platform.Consistent with consumers' behavior on other sharing economy platforms, couchsurfers may assume different roles, depending on their goals, interests, and their available resources. Couchsurfers may exclusively host, exclusively stay at other members' houses when traveling, both host and stay, or neither host nor stay, instead simply interacting with other couchsurfers in hangouts (i.e., a feature that allows members to easily find nearby couchsurfers to meet up with) or at events (i.e., gatherings organized regularly by couchsurfers). Research Methods Data CollectionTo investigate how platform consumers orchestrate their experiences and cocreate value in the sharing economy, we used a multimethod approach that combines netnography, participant ethnographic observations, and interviews. Netnography is a qualitative method for investigating online groups, communities, and cultures for marketing purposes ([22]). It requires participant observation in existing online environments and allows the researcher to unobtrusively collect data on consumers' culturally embedded experiences.By observing and participating through Couchsurfing and related websites, as well as social media platforms, we developed familiarity with the Couchsurfing experience, as lived by consumers. We created profiles on Couchsurfing.com, downloaded the Couchsurfing application to our smartphones, logged in regularly to check couchsurfers' profiles, read discussions in Couchsurfing groups, and followed links offered by couchsurfers while writing field notes and systematically collecting data about experience cocreation. In addition to observing and interacting with couchsurfers, we created an interactive research web page that described this research project and invited consumer participation. We also searched for other content created by couchsurfers, such as posts on Reddit, YouTube, and blogs. The netnographic research lasted 12 months (July 2016 to July 2017), but we continued accessing Couchsurfing-related websites sporadically until December 2019.The netnographic participant observation seamlessly became ethnographic when one of this study's authors organized two Couchsurfing events to meet local hosts and travelers. Through the platform, this researcher interacted with several people who planned to attend the events and then met some of them face-to-face during one event. We also posted upcoming trips on our Couchsurfing profiles and interacted with potential hosts in the cities where we were planning to travel. We met some of these couchsurfers in hangouts during their travels, stayed at others' homes, and hosted travelers who were visiting our local areas.Complementing these participant ethnographic observations, we conducted interviews with 40 couchsurfers (see Table 1). We recruited participants through this research project's web page, recommendations provided by members of our personal networks (who are couchsurfers), and snowball sampling. We developed an interview guide that enabled us to capture couchsurfers' perceptions of value cocreation through their experiences ([18]). With the help of two trained assistants, we conducted interviews in person and on Skype and voice recorded and transcribed them. We performed preliminary analyses following each interview and conducted additional interviews until new data became redundant.GraphTable 1. Description of Informants. 1 Notes: IT = information technology; N.D. = not disclosed.Finally, we systematically searched for media reports about Couchsurfing and books written by couchsurfers. Although the media reports contain important information on how the general public has perceived Couchsurfing over time, the books written by couchsurfers contain detailed descriptions of the experiences they cocreate in the network (see the Web Appendix). Through this extensive fieldwork, we amassed a large volume of data in multiple formats, such as field notes, texts, videos, pictures, and audio files. A trained research assistant downloaded data related to value creation and experiences from the web pages we had observed and prepared them for coding and analysis using qualitative data analysis software. Data AnalysisWe engaged closely with the entire data set. Consistent with inductive reasoning from grounded theory ([ 8]), we initially conducted emergent coding to identify how different aspects of Couchsurfing's consumer experiences create value for consumers and the platform firm. This approach made salient the multiple actions that consumers enact as they attempt to overcome the cocreation challenges. We refined our analysis and clustered these actions into types by identifying the similarities and differences and by noting the challenges consumers seemed to be solving when enacting each action. At this stage, it became clear to us that consumers were going beyond just collaborating with others to cocreate value for themselves and the community ([40]). They purposefully found workarounds for the challenges they faced when cocreating. This led us to use ""orchestration"" and ""orchestration work,"" which refer to the coordination of cocreation by multiple actors ([19]) as sensitizing concepts for discussing the data. Thinking about orchestration work as a series of actions allowed us to identify theoretically meaningful patterns that were reciprocally adjusted to the literature ([24]).In searching for patterns among how consumer actions worked to address the challenges of cocreation, we identified four orchestration mechanisms. We purposefully searched for cases that could dispute our framework, discussed discrepancies, and used several descriptors until we settled on those that clearly delineated the data patterns. In doing so, we adjusted our categories to progress toward a theoretical understanding of the phenomenon in a way that was consistent with the data.Finally, with this grounded understanding of consumer orchestration work, we reconnected to existing theories of consumer experience, value cocreation, and the sharing economy, and then examined the premise that consumer orchestration actions could be mapped onto the known sources of value creation for firms ([56]). This last stage was an iterative process in which we independently classified excerpts, discussed data exemplars, and used different forms of data as triangulation tools while adjusting their interpretations to the literature ([43]). How Consumers Orchestrate Experiences to Cocreate Value in the Sharing EconomyAs consumers attempt to address the challenges of cocreating experiences in the sharing economy, they engage in multiple, often overlapping actions. These actions and their underpinning mechanisms constitute consumer orchestration work, as they assist consumers in overcoming challenges to cocreating unique, valuable experiences for themselves. We identified four mechanisms of consumer orchestration work: C2C alignment, rewiring relations, trust investment, and network experimentation (see Table 2), clustering consumer actions into groups according to the key challenges that these actions aim to address. In this section, we introduce these mechanisms and their respective orchestration actions and illustrate them with examples from our data set. For the sources of numbered quotes cited in this section and additional examples, see the Web Appendix.GraphTable 2. Challenges, Roadblocks, Orchestration Mechanisms, and Actions. The Mechanism of C2C AlignmentWhen platform consumers cocreate in the sharing economy, they have expectations regarding what they will be able to achieve and how. Because sharing economy consumers are highly heterogeneous, these expectations often differ largely between cocreation partners. For example, a Couchsurfing host, Mariam, struggled to understand a cocreation partner's expectations and to have her expectations fulfilled, as she shared on a Couchsurfing group:I have been hosting just for a couple of weeks and the experiences were incredible until today. I accepted a female surfer from Ukraine. She is 29 and she seemed really nice when she sent me a request. But when she arrived, she is all shy, not talkative at all. I invited her to hang around (she said no because she wanted to charge her phone, which is acceptable), I invited her for pizza which she refused, I also invited her for a beer and she said no. I have been having these amazing surfers and I am not sure what to do now. Maybe I am missing something? (Initial post on thread ""No interaction/adjusting interactions"")We uncovered several actions through which platform consumers (both guests and hosts) overcome common roadblocks to cocreation (such as those that Mariam describes) and navigate the challenge of heterogeneity. We detail these actions next, grouping them into similar types, as illustrated by examples from our data set.""Screening"" refers to actions that help consumers select the desired cocreation partners for an experience. These actions include applying search filters (""As a single guy, I do filter out if they're traveling with their partner"" [ 1]), checking social media profiles (""For those with no references, I try to connect through Facebook or something where I can verify their IDs"" [ 2]), or validating references and identity (""Do you know Bemelieu? He PMd me for a couch"" [ 3]), among other actions.""Cueing"" refers to actions that cocreation partners or potential partners undertake to orient the cocreation of their experiences in desired ways. Cueing includes orchestration actions such as listing in one's profile expectations regarding partners (""I love cooking! We can cook together and share good recipes"" [ 4]), messaging before a stay to fine-tune expectations (""Hi again, just so you know, I am working from eight to five but we have a spare key that you can borrow"" [ 5]), and enacting welcoming rituals (""When someone arrived, someone in the house would show them around the house quickly, say 'if you can see it, you can use it,' and hand them a copy of the keys"" [ 6]), among other actions.""Flexing"" refers to actions that imply concessions and adaptations that cocreation partners make concerning one another while interacting to cocreate their experiences. This group of orchestration actions includes proposing or accepting changes to planned activities (""Minni also hosted me a night as I was stuck in SF, even though his profile was on 'no couch.'"" [ 7]), requesting/offering additional resources from/to a partner (""I said: OK, if you want to stay longer, stay, no worries"" [ 8]), and making adjustments to one's environment, habits, or routine to accommodate partners' needs (""I had a host who gave me and my sister his bed and slept on the living room couch because he didn't want to disturb us when he [left] to work early in the morning"" [ 9]), among other actions.""Buffering"" refers to actions taken by one or more partners to overcome heterogeneity impediments to value cocreation and to attenuate the potential loss of reputation due to setbacks in their cocreation of experiences with heterogeneous partners. Buffering includes actions such as establishing boundaries (""I tried to kiss her. She, was like, 'No, no, no—I don't want to make it awkward.'"" [10]), apologizing (""I apologized for missing dinner that they had cooked for us"" [11]), offering peacemaking gifts (""One girl, despite being certifiably crazy and her stay at my home being a little slice of hell, showed up with free passes to … Dirty Dancing at a theater near my work"" [12]), or impeding further deterioration by ending the cocreation (""I've cut visits short, and only once have I just kicked someone out but that was due to some egregious s—"" [13]).These orchestration actions happen at the micro-level of cocreation, that is, within a particular cocreation experience. They allow consumers (both hosts and guests) to identify potential partners who are likely to be aligned with them and adjust to cocreation partners who are not. To capture this function, we categorized screening, cueing, flexing, and buffering actions under the mechanism C2C alignment. We define C2C alignment as the mechanism that enables platform consumers to overcome the challenge of heterogeneity by aligning experiential elements (i.e., expectations, interactions, and responses) with those of heterogeneous cocreation partners.Take Hilkka, for example, a host who shared her cocreation knowledge in response to Mariam's question in the Couchsurfing ""Advice for Hosts"" group:Everybody has different expectations. That's why I tell in my profile [cueing] that I like independent surfers who manage on their own in the downtown. Before I used to guide my guests, taking them to the city center and showing them around for a couple of hours. Having done sightseeing many, many times over the years, I appreciate it if my surfer will do it without me. If it seems that my surfer enjoys my company (and vice-versa), we spend more time together, otherwise it's enough for me to have some interaction in the evening, as well as in the morning at breakfast. There have been surfers who are a little shy, not too willing to have a chat with me, but I don't mind as long as they are friendly and respectful [flexing]. It is difficult to say how much (or little) interaction is good, it varies from case to case. It depends on the surfer and me, how we feel and get along, on mutual interests etc [screening]. Sometimes it's better to limit the interaction to small talk [buffering], although more often it's just great to learn what my surfer has to tell e.g., about her country, family and travels.Hilkka's post highlights several actions[ 8] that she undertakes as a host to align her expectations with those of potential and actual guests to cocreate experiences, thereby offering an illustration of how the many actions of orchestration through C2C alignment are entangled and work for the common purpose of assuaging the potential differences among cocreators. The actions highlighted in Hilkka's account, when effective, leave cocreation partners with the impression that they ""get along"" and that the cocreated experiences were ""great,"" despite the heterogeneity among cocreation partners. The Mechanism of Rewiring RelationsAs consumers partner to cocreate in the sharing economy, they develop platform-mediated relationships that are a hybrid of transactional exchanges and communal forms of sociality. Navigating such relations and shaping them in ways that are conducive to the creation of valuable experiences is challenging. As a guest, for example, Phil was on a journey through West Africa. While in Ghana, he needed information about Côte d'Ivoire, the country he would visit next. He chose to connect to other consumers on the Couchsurfing platform to obtain information:I have joined groups and introduced myself, explaining why I was traveling to a particular country or city. Most guidebooks on Cote D'Ivoire are worthless. They are filled with information that is outdated and unreliable. Some have not been updated since the country was at war. ([32])In planning his trip, Phil needed additional information, and it seemed to him that connecting to other guests and hosts on the Couchsurfing platform could address this need. However, some Couchsurfing consumers want the close, identity-shaping relations of communities; others, the impersonal, transactional relations of commercial settings; and others, a mix of both, depending on the situation. Thus, to achieve his instrumental goal of collecting information for his trip, Phil faced roadblocks associated with the need to form social relations with other couchsurfers whose social goals and interests were very different from his. Phil had to count on the platform's affordances to establish and maintain relationships in this hybrid environment. We identified four types of orchestration actions that help platform consumers overcome the roadblocks associated with the challenge of networked sociality.""Interest grouping"" refers to orchestration actions through which consumers use platform affordances to create explicit links to existing or imagined groups within the collective to access information and leverage shared values and interests. We found that the multiple interest groups hosted on Couchsurfing vary widely and can be based on identity (""Queer Couchsurfers, 53,070 members"" [14]), the purpose of an experience (""Worldwide Volunteering, 59,402 members"" [15]), information seeking (""Airlines: low-cost, budget, cheap flight, 83,504 members"" [16]), location (""South America, 32,914 members"" [17]), or needs (""Help! Need a place in Lyon for today"" [18]), among other themes.Most group participants are not committed to frequent participation, and most threads are started by new members. Unlike online communities, these groups do not cultivate communitas ([50]) or foster a sense of belonging among members. Instead, these fleeting associations are appealing because they help sharing economy participants locate sources of complementarities in the larger Couchsurfing network. For instance, those traveling to new places can pool local information and resources for cocreation that would otherwise be unavailable and identify potential cocreation partners with whom they can develop closer, more personal relationships.""Lifestyle signaling"" refers to orchestration actions that use platform affordances to help consumers connect cocreated experiences to a specific lifestyle. These actions consist of a subtler form of identification than interest grouping and allow consumers to connect identity projects to experiences cocreated in a network of which each participant is, at the same time, unique and interchangeable. Under this type, we include actions such as highlighting in one's profile skills that have been developed through participating on the platform (""As a keen amateur photographer, I enjoy chronicling our Couchsurfing adventures in pictures, and introducing our guests to studio photography in our little home studio"" [19]) and showcasing identity or creative content that is based on one's experiences with the platform (""We have recently published a book about our Couchsurfing experiences"" [20]), among others.""Enclaving"" refers to orchestration actions that use platform affordances to help create ephemeral communal spaces in which consumers experience the advantages of network proximity, despite the ephemeral nature of sharing economy interactions. Similar to consumers' efforts to build a hyper community ([21]), enclaving actions orchestrate experiences around the promise of temporary but intense communality. Enclaving actions include proposing and attending hangouts with strangers (""I have two hours before my train leaves. Wanna grab a beer?"" [21]), establishing regularity in local meetings among strangers (""Weekly CS LYON Monday Meeting"" [22]), and organizing extraordinary events (""A camp is a multiday event where couchsurfers from all of the world come together and hang out for a whole weekend"" [23]).""Reconciling"" refers to orchestration actions that use platform affordances to attempt to reconcile relations based on quid pro quo and generalized reciprocity. These orchestration actions reinforce both the quid pro quo nature of exchanges and the ties among potential cocreation partners. These actions include toning down negative reviews as a way of preserving reputation and future relations while still maintaining the quid pro quo nature of the feedback (""I see a lot of people leave neutral references when they actually want to leave a negative reference but have mixed feelings"" [24]), using the platform to achieve personal goals while strengthening communal bonds (""I am hosting in my city because I am kind of new here and it is difficult to meet people"" [25]), and organizing activities out of self-interest and that enhance communal activities. Local tourist guides are often involved in organizing pub crawls via hangouts; while they do this for personal gains, they also help foster communal bonds or even seek advice from platform consumers about how to navigate conflicts between communal and transactional logics (""How do you handle situations like this [host seducing guest]? All these people seemed to like us and wanted us to stay with them. We on the other side felt in a very awkward situation"" [26]).These types of actions—interest grouping, lifestyle signaling, enclaving, and reconciling—are categorized under a mechanism we call ""rewiring relations."" We define rewiring relations as the mechanism that enables consumers to overcome the challenge of network sociality by using platform affordances to navigate and integrate the communal and transactional aspects of their relationships. The mechanism helps consumers leverage platforms' affordances and rework the sociality in the network to better suit their individual goals.Consider how Phil describes his engagement with the utility of joining Couchsurfing groups nested within the Couchsurfing platform:While I was still in Ghana, I joined the Cote D'Ivoire group and began searching through previous posts [interest grouping]…. Twenty minutes of browsing through the group and I know what to expect when traveling overland, where I should listen to reggae in Abidjan, and whether it's dangerous in the North of the country. I posted a question of my own about overland travel from Abidjan to Bamako and received some great advice. Within the Cote D'Ivoire group, I noticed several members were particularly active. Their profiles provided a lot of information about them, but what they said in the group was more revealing [lifestyle signaling]. I got a sense for who was proficient in English. I was hoping to unearth my French after several years of neglect, but I liked the idea of staying with someone who spoke English when I first arrived. One particular member spoke English and French and had posted some funny, enthusiastic, and informative messages. I contacted her, and she became my first host in Cote D'Ivoire [reconciling]. I've been staying with her and her boyfriend for almost three weeks. I got in on a meetup for reggae lovers at a bar called Parker Place. Missing my weekly dose of Patty Boom, my favorite reggae spot in DC, Parker Place has been an excellent stand-in, and it has allowed me to meet some awesome Ivorians who share the same musical tastes as me [enclaving]. ([32])Phil's post highlights several orchestration actions that he enacted through the platform to benefit from being connected to the collective. His temporary association with the Côte d'Ivoire group helped him access updated and personal information on potential experiences (""overland travel from Abidjan to Bamako""). His sharing of activities and goals in the group (""introduced myself, explaining why I was traveling, describing my trips"") helped him signal himself to others as a genuine couchsurfer and to find potential hosts who have desirable characteristics (i.e., spoke English and French and were ""funny, enthusiastic, and informative""), and his attendance at events organized through the platform (""meetup for reggae lovers"") allowed him to cocreate experiences with local couchsurfers who share his interests. These actions enable consumers (both guests and hosts) to navigate the challenge of network sociality to accommodate communal and transactional relations, which shapes sociality in the sharing economy collective in ways that enable the cocreation of valuable experiences. The Mechanism of Trust InvestmentWhen cocreating in the sharing economy, consumers face roadblocks associated with the challenge of interpersonal trust, which makes it difficult to cocreate valuable experiences while collaborating with other platform consumers whom they do not know. Guest Janaina, who opened a thread called ""Trusting Your Host"" on the ""Advice for Surfers"" group on the Couchsurfing website, explained how challenging it was for her to trust strangers in cocreating intimate experiences:This will be my first time Couchsurfing alone overseas or couchsurfing at all I should say. So, my friends and family are very skeptical about Couchsurfing especially if it's a male being the host. Just looking for some advice on staying with a male; even if I read good reviews [I am] still a little nervous just because of a totally random person!… How do you really trust the host, leaving your bags there and everything? I almost feel safer just looking for a family or couple rather than a single person. Thanks.These roadblocks to cocreation, Janaina noted, are not uncommon among couchsurfers. We identified three types of actions that platform consumers (guest and hosts) engage in that help them navigate the challenge of interpersonal trust in cocreating with strangers.""Revealing"" refers to actions that allow platform consumers to signal their integrity and assess that of potential cocreation partners. Revealing helps address a common trust roadblock that consumers face, which is determining how to assess the integrity of potential cocreation partners who may have different values, norms, and behaviors. Revealing includes requesting additional information from others (""For safety reasons, we want complete profiles and personal requests. We want to know who we are hosting and why they chose us"" [27]), providing additional information about oneself in profiles, discussion forums, messages, and face-to-face interactions (""When a surfer sends me a link to his/her VLOG and after reviewing a few videos,… my decision to host is usually immediate"" [28]), and asking friends for personal testimonies (""If you are new to [Couchsurfing] and don't have any reviews, ask your friends who use the service to write you a review and describe you as a friend"" [29]). Cultivating reviewsA common roadblock that consumers face when cocreating with strangers is the need to demonstrate how reliable they are to potential cocreation partners. We found that consumers overcome this roadblock by engaging in orchestration actions that signal consistency in behavior and reliability. We refer to this type of action as ""cultivating reviews."" This includes actions such as hosting friends in exchange for reviews (""The host wants to be a surfer when they travel, so they host to build more reviews so that they can get accepted when they send out Couchsurfing requests"" [30]), asking for reviews of a specific kind (""A host asked me to make sure I made specific comments about the freedom he gave to all guests staying at this place"" [31]), and boosting reviews (""[Couchsurfer] asked me to leave him a reference so that he could achieve a better gender balance in his references"" [32]).""Scaffolding"" refers to orchestration actions that allow cocreation partners to progressively trust one another. Scaffolding results in familiarity, security, and control, as it reduces trust gaps by allowing participants to make gradual assessments of their counterparts' reliability and integrity. Scaffolding actions include proposing short experiences before a stay (""Later he asked if he could stay tonight and I said yes, then he asked for a second night and I accepted that as well"" [33]), meeting cocreation partners in public spaces first (""We met at the station and then headed to his home"" [34]), using temporary gatherings as a way of getting to know partners (""I went to a hangout to try to find a couch"" [35]), creating if/then rules for interactional behavior (""I don't even write a request to these men [who only host women]"" [36]), restricting access to personal resources (""Laundry at the house is not on offer, but I can assist you in finding a coin laundry for you to get those clothes washed!"" [37]), and pacing responses (""I see this constantly, mostly from young female surfers, where they say they are receiving multiple requests and will respond later or something similar to that"" [38]), among other actions.These types of actions—revealing, cultivating reviews, and scaffolding—enable consumers to reduce risks in cocreation. To capture this function, we categorize these actions under a mechanism we call ""trust investment."" We define trust investment as the mechanism that enables platform consumers to address the challenge of interpersonal trust by managing platform resources to mitigate the risk of engaging in one-off interactions with strangers in the sharing economy. In interpersonal relationships, trust develops when ""one party has confidence in an exchange partner's reliability and integrity"" ([30], p. 23); thus, it signals the degree to which a person can depend on others to do what they say they will. Whereas the actions of C2C alignment and rewiring relations may also occasionally end up reducing risk among cocreating consumers in a sharing economy collective, trust investment actions are enacted specifically to overcome the challenge of interpersonal trust.Take, for example, how host Marie advised Janaina to host others to build a trustworthy track record rather than worrying about potential unsafe hosts:[You should host, but] if you really really really cannot host, please show a CS member around … meet up and share a meal and conversations [scaffolding] … i.e., invest some of your time and resources,… And that way, get some references [cultivating reviews], your potential future host wants to be safe with you, too! as someone said above: if you request a couch from a host with references: you could double check with former guests about safety. but if you have none: how could I, your host, double check to make sure I am safe with you? after all you'll be invading all of my privacy - while all I could potentially invade would be your backpack :-)"" (Marie, comment on the ""Trusting your host"" thread in the Couchsurfing ""Advice for Surfers"" group)Marie recommends orchestration actions that work as trust investments. She recommends that the new couchsurfer make efforts to meet other consumers and share resources with them (""a meal and conversations"") as a way to progressively build a reputation (""get some references"") and signal her integrity and reliability on the platform. She also explains how references are not to be taken for granted, as they are indicators of a potential cocreation partner's reliability but should be mobilized to reveal further information (""doublecheck with former guests about safety"") that could help reduce the risks. The Mechanism of Network ExperimentationPlatform consumers seek personalized experiences in the sharing economy. To achieve these, they need to continuously work with cocreation partners, often improvising as they cocreate and adapting for personalization. Take, for example, the case of Nahim, a self-described ""couchsurfing-drifter"" (39) who was using Couchsurfing to find accommodation in Afghanistan. Given the country's weak security and deteriorated tourism infrastructure, Couchsurfing emerged in the region as an alternative (Kabul has approximately 1,000 Couchsurfing hosts), allowing local hosts to quench their thirst for cosmopolitan experiences and intrepid travelers to find hospitality. However, cocreating experiences in unconventional conditions is not easy. Nahim faced roadblocks associated with not knowing the hosts' culture, not being dressed adequately, and not understanding the language. We identified three types of orchestration actions that help platform consumers (both guests and hosts) overcome roadblocks associated with personalized experiences, such as those experienced by Nahim, and cocreating unique value outcomes in the sharing economy.""Creative resourcing"" refers to the type of actions whereby consumers introduce new resources to cocreate experiences that are unique and personalized and share these experiences on the platform. These actions include making new resources available for cocreation, which extends the range of cocreated experiences (""[Host] hosted me for two months at the university housing building. I had my own room, and even attended classes [laughs]"" [40]) and offering different sets of resources within a traditional experience (""The [host] had coffins that went into the wall, would pull them out, and it became a bed"" [41]), among others.""Role improvising"" refers to the type of actions whereby cocreation partners step into new roles and scripts while cocreating to extend the value potential of their experiences. We identified a series of actions for this type, including taking on new roles and scripts to enhance the range of cocreation activities (""[The host's] mother is sick, has cancer, so I … chose to spend more time with him, talk to him, just like a psychologist"" [42]) and immersing oneself in these roles and scripts to prolong or intensify the cocreation experience (""we are still in touch, and [guest] invited me to his wedding in Brazil"" [43]). Through these actions, cocreation partners move away from the normative guest and host roles to improvise as confidantes, psychologists, and stylists, among other roles.""Repurposing"" refers to orchestration actions whereby consumers introduce new goals or value propositions for interactions that are enabled by the platform. Under this label, we grouped actions such as using the network and the platform for cocreation purposes other than those endorsed by the platform firm (e.g., ""sex surfing"" [44]) and using current resources to repurpose activities toward achieving additional goals (""[Couchsurfers] will hire a bus to visit a remote area outside the city [as a Couchsurfing event]"" [45]), among others.These types of actions—creative resourcing, role improvisation, and repurposing—constitute a mechanism we call ""network experimentation."" We define network experimentation as the mechanism that enables platform consumers to overcome the challenge of personalizing cocreation by trying new resources, roles, and goals when cocreating experiences, thus extending the possibilities for the cocreation of unique, personalized, and valuable experiences. Through network experimentation, orchestration actions increase the field of potential expectations, interactions, and responses among cocreation partners in the network. Consider, for instance, how the couchsurfers in Afghanistan engaged in actions of network experimentation to help Nahim and other couchsurfers overcome the challenge of personalizing cocreation, as recounted in a blog post covering Nahim's travel experience:[He] managed to hitchhike through Iraq by displaying a sign in Arabic to passing drivers, written by one of his hosts [role improvising]. After arriving in the western Afghanistan city of Herat, he became acquainted with some local members of the Taliban, whom he described as ""actually really nice people."" His disguise [to travel safely in Afghanistan] consisted of a white shalwar kameez (traditional Afghan clothing) and a taqiyah (cap for observant Muslims). The clothing was provided by his Couchsurfing hosts [creative resourcing], who also taught him how to pray to Mecca [role improvising], should the need arise. ([23])Nahim's story highlights how orchestration actions that operate through the mechanism of network experimentation help consumers personalize cocreation with strangers. Nahim reported on the incorporation of new resources into cocreated experiences (""offering disguise clothes to help him reach his goals"") and described how his hosts improvised by stepping into a new role (""writing signs in Arabic for his hitchhiking"" and ""teaching him how to pray""), which provided value outcomes beyond the usual Couchsurfing experience.Overall, our findings point to four overarching mechanisms of orchestration work and 14 specific actions that consumers engage in to cocreate value in the hybrid setting of sharing economy platforms. Figure 1 consolidates consumer orchestration work, detailing the actions and mechanisms that help consumers cocreate unique and valuable experiences for themselves. Our focus thus far has been on examining the nature of consumer orchestration work on platforms. As we uncovered these mechanisms, we also found that they connected to well-known sources of value for platform firms. The next section explains how orchestration work creates value for platform firms.Graph: Figure 1. How consumers orchestrate experiences and cocreate value in the sharing economy. How Consumer Orchestration Work Creates Value for Platform FirmsAs orchestration work enables value creation for platform consumers (service providers and users), we found that it also appears to activate known sources of value for platform firms. In this section, we identify how consumer orchestration work leads to efficiency, complementarities, lock-in, and novelty ([56]) in the platform environment.Efficiency, which refers to cost savings enabled by interconnections, is one of the primary value drivers for platform firms ([56]). Orchestration work contributes to increasing the efficiencies associated with cocreating on the platform. As consumers align their expectations, interactions, and responses with those of heterogeneous cocreation partners through C2C alignment, the cost of organizing exchanges among peer-service providers and users is reduced. C2C alignment is also likely to reduce costs arising from unsuccessful cocreation partnerships (e.g., consumer dissatisfaction and defection; reputation damage). Specifically, screening enhances consumers' likelihood of finding partners who cocreate in ways that these consumers consider desirable, thereby reducing the overall effort needed to integrate resources. Cueing allows cocreation partners to signal to one another the type of experience they envision, reducing the risk of misalignment during cocreation. Flexing allows cocreation partners to negotiate access to and provision of resources to better accommodate their individual needs. Buffering ensures that experiences gone wrong are dealt with quickly, preserving some of the cocreated value and preventing the escalation of tensions. These orchestration actions make consumers' cocreation in the sharing economy more efficient and create value for the platform firm by further reducing direct and indirect costs associated with organizing exchanges among platform consumers.Complementarities, which refer to the ""value-enhancing effect of interdependencies"" ([56], p. 21), are another important source of value for the platform firm. Orchestration work leads to complementarities in cocreation by spurring beneficial interdependencies among platform consumers. Rewiring relations leads to complementarities by creating opportunities for generating synergies with others in the platform. Specifically, interest grouping and lifestyle signaling build on the idea that close-knit groups can exist in the sharing economy, even though we found, in line with prior research ([ 4]), that most consumers do not consider these collectives as being communities. Such groups can provide opportunities to establish desired relationships within the collective and reconnect identity projects to cocreated experiences, adding value to these experiences. Enclaving provides actors with opportunities to experience intense, albeit temporary, communal sociality at the local level. This intense sociality is likely to enhance opportunities for value cocreation through the platform. Reconciling helps consumers use the platform connections to achieve both individual and communal goals. These orchestration actions increase complementarities by capitalizing on the existing interdependencies in the network. As the network becomes more valuable to consumers, the platform firm that hosts it may be able to capture additional value in turn.Lock-in, which refers to ""business model elements that create switching costs or enhanced incentives for business model participants to stay and transact within the activity system"" ([56], p. 21), is yet another source of value for the platform firm. One important way of generating lock-in in sharing economy platforms is to reduce the risk of cocreating with strangers ([53]). By improving interpersonal trust among platform consumers, orchestration work increases the pool of potential trustworthy cocreation partners, offering additional incentives for platform consumers to stay and transact within the platform. Trust investment actions help platform consumers become better at signaling and assessing the integrity and reliability of those with whom they will cocreate. Revealing increases opportunities for consumers to promote their ability to integrate resources and cocreate with others, promoting themselves as low-risk partners. Cultivating reviews is indicative of the likelihood of the success of future collaborations with cocreation partners. Scaffolding enables cocreation partners to slowly test their ability to cocreate together and adapt their behavior to reduce the risk in extended cocreation efforts. These orchestration actions promote lock-in by increasing the level of interpersonal trust that exists among platform consumers. A higher level of interpersonal trust is a source of value for platform firms because when perceived risk in cocreation is reduced, the platform is perceived as safer ([53]), and platform consumers are likely to increase their engagement for longer periods of time.Novelty, which refers to ""degree of business model innovation that is embodied by the activity system"" ([56], p. 21), is also a source of value for the platform firm. Consumer cocreation becomes a source of innovation for the platform firm when it helps the platform incorporate resources, roles, and goals that are not only novel but also meaningful to its consumers. Actions of network experimentation enable platform consumers to explore new ways of cocreating unique experiences with more personalized value outcomes. Creative resourcing encourages platform consumers to share their creative use and integration of resources, resulting in a unique experience for those involved and in novel configurations of resources for cocreation by others in the network. Role improvising helps consumers step into new cocreation roles, allowing for diverse and unique sets of scripts to be available for platform consumers. Repurposing creates new opportunities for value cocreation by introducing new value propositions into the network; when these become ubiquitous (as is the case of ""sex surfing""), they allow for a different set of repurposed activities to emerge. These orchestration actions lead to value for the platform firm by aggregating new resources, scripts, and purposes to the platform offering, which, in turn, becomes attractive to a larger number of potential consumers.Finally, although we have noted clear links between the actions of each identified mechanism—C2C alignment, rewiring relations, trust investment, and network experimentation—and well-known sources of value for the firm ([ 2]), we highlight that the actions of each orchestration mechanism can activate multiple sources of value for firms. For example, although screening has the immediate effect of creating efficiencies through the selection of suitable partners, it may also lead to further complementarities in the platform if consumers identify cocreation partners who have resources that complement their own. Screening may also promote lock-in as consumers become increasingly skilled at identifying ideal partners on the platform and are likely to remain loyal to this platform; they can then continue using their screening skills to reduce the risks associated with cocreating via the platform. For more entrepreneurial platform consumers, screening can result in novelty for the platform firm when these consumers learn how to select partners who are more likely to propose or welcome atypical experiences or disseminate their novel, cocreated experiences to others on the platform. As with screening, other orchestration actions can also be linked to more than one known source of value creation for a firm ([ 2]). The capacity of each orchestration action to impact multiple sources of value for platform firms highlights the value-creating power of consumer orchestration work. Marketing Implications Implications for Platform Firms in the Sharing EconomyA key marketing issue for the managers of platform firms is how best to capture the value that is cocreated by platform consumers to achieve long-term sustainability in the sharing economy ([12]). Despite the enormous potential of consumer cocreation, many platform firms still approach cocreation from a traditional business mindset; they aim to manage the onstage aspects of consumers' experiences rather than assume a backstage role and focus on developing support processes and structures ([ 9]). We propose that platform firms can improve the value they obtain from consumer collectives in the sharing economy by understanding and supporting consumer orchestration work, which is geared toward helping consumers independently overcome cocreation roadblocks.Platform consumers engage in orchestration work to address the challenges they face while cocreating experiences in the hybrid logics of the sharing economy. To best support consumer orchestration work, managers of platform firms must identify these roadblocks and know how to leverage orchestration actions and respective mechanisms. The case of Couchsurfing, with its low level of platform control over consumers' cocreation, allows us to better understand the types of actions and mechanisms that consumers put into place to overcome these challenges and cocreate value for themselves.By facilitating C2C alignment, platform firms can help consumers overcome the challenge of cocreating with heterogeneous partners. For example, this challenge is evident with Airbnb, which, similar to Couchsurfing, has a vast and diverse network of consumers ([26]). Airbnb encourages hosts to be upfront in their online profiles and initial message exchanges. It also encourages guests to pay attention to cues provided by hosts. Drawing on our findings, Airbnb could further support consumers in dealing with the challenge of heterogeneity by encouraging screening through additional filters that more specifically account for expectations and preferred ways of cocreation (e.g., allowing guests to indicate their desired amount of contact/conversation with the host). Airbnb could further support cueing by reminding hosts and guests to complete profile tabs associated with preferred modes of interaction (e.g., by asking questions such as ""Chat over coffee or no talk before breakfast?""). Furthermore, Airbnb could encourage its consumers to engage in flexing by educating them on the need to accommodate variability while cocreating (e.g., disseminating curated, user-generated how-to videos that address common misalignment issues, such as ""Three ways to deal with guests who leave your kitchen messy""). Finally, Airbnb could help consumers reduce the impact of cocreation mismatches through buffering by providing guidelines on how consumers can curtail issues and recover from negative experiences (e.g., crowdsourcing a flowchart for common cocreation problems and ways to address them). These initiatives would allow this platform firm to increase opportunities for its consumers to pursue cocreation efficiencies while maintaining orchestration work under consumers' control.Platform firms can help consumers overcome the challenge of networked sociality by helping them rewire relations for cocreation. For example, Tinder successfully developed a platform for high-involvement customer experiences ([42]), yet it has been criticized by consumers and the media for making someone's search for a partner too transactional. Tinder could address these critiques by helping its consumers reconcile the app's transactional logics (e.g., swipe left/right) with communal logics and the desire for more meaningful relationships. It could, for example, assist consumers with using the platform for interest grouping (e.g., offering tags for members who are into pet walking or cooking together), and assist consumers in lifestyle signaling by providing a means for them to connect over preferred hobbies or passions (e.g., forums for those into watching and discussing movies about art). It could also enable enclaving by supporting the creation of spaces that enable closer relationships among platform consumers (e.g., helping entrepreneurial consumers promote local singles' face-to-face night and facilitate these events by enabling communication about them on the platform). Tinder could also support consumers in their work of reconciling personal goals with the need for strengthening communal bonds. For instance, Tinder could create a forum for storytelling about successful and failed hookups, a bonding activity that often encourages sociality and sharing (communal goals) while equipping consumers to become better at finding dates for themselves (transactional). This type of forum currently happens outside this platform (e.g., on Reddit), which demonstrates that consumers already engage in orchestration work.Overall, these initiatives could help consumers better utilize the power of social relations to address both their quid pro quo hookups and their need for meaningful social connections. Couchsurfing and other platforms can assist consumers in overcoming the challenge of interpersonal trust by helping them reduce the risk of cocreating experiences with strangers. At Uber, for instance, the perceived lack of authenticity (withholding one's true self) is a major challenge in building interpersonal trust ([15]).While trust mechanisms such as protection insurance and identity verifications are fundamental to raising consumer trust in the platform and its consumers ([25]), platform firms can increase interpersonal trust by supporting and leveraging the trust investments made by consumers to overcome their interpersonal trust issues. Thus, supporting consumers' work to reveal more about themselves through profiles, tags, and prompts, safely and progressively (scaffolding), and helping them cultivate authentic reviews about their cocreation behavior, could increase interpersonal trust among consumers of the Uber platform.Similarly, BlaBlaCar, a ride-sharing platform that matches empty car seats with potential passengers looking for long-distance rides, has a well-researched history of helping platform consumers (drivers and riders) overcome the interpersonal mistrust that usually exists when cocreating with strangers. BlaBlaCar supports revealing by encouraging consumers to share more information about themselves in their profiles and to connect this information with their existing online identity (e.g., Facebook profile). Research has found that 88% of BlaBlaCar consumers highly trust a member who has a complete digital profile, which is higher than the trust levels of colleagues or neighbors ([27]).Our findings suggest that BlaBlaCar could further boost revealing by encouraging consumers to share more specific information about their cocreation preferences during interactions (e.g., ritualizing the sharing of cocreation stories as icebreakers, offering platform-specific personality quizzes, allowing consumers to associate their profile with a particular consumer persona—chosen from a pool of existing personas). Currently, BlaBlaCar asks its consumers to rate one another after having shared higher-stakes, real-life, offline experiences. Our findings suggest that BlaBlaCar could further leverage consumers' work of cultivating reviews by providing badges that consumers could win or gift each other for each review, or by allowing consumers to create additional questions to be asked by their reviewers. This would help future cocreation partners identify behavioral consistency (e.g., if John has great taste in music, he could get a ""great music"" badge and ask partners, ""What did you think of your driver's soundtrack?"" rather than the generic ""What did you think about this experience?"").BlaBlaCar currently encourages its consumers to speak on the phone before agreeing on a ride. We suggest that the platform could further support consumers' scaffolding work by highlighting opportunities for them to progress into more trustworthy relationships with others in the platform. For example, BlaBlaCar could use notifications to prompt cocreation partners to discuss things ahead of time that could potentially lead to pain points along the customer journey (e.g., after agreeing on a ride and a day before the trip) to allow interpersonal trust to be built through multiple interactions. Overall, these initiatives would support consumer orchestration work aimed at increasing interpersonal trust among the platform's consumers.Finally, platforms can help consumers overcome the challenge of personalizing cocreation with strangers by supporting them as they try new resources, roles, and goals when collaborating with others to cocreate experiences and, thus, extend the possibilities of creating unique and valuable experiences for themselves and others in the platform. Platform firms often propose new features and processes to continuously innovate and personalize the customer experience. Platforms can achieve these goals faster by leveraging the network experimentation conducted by consumers in their quest to create unique personalized experiences. For example, TaskRabbit, a platform that connects consumers who need help with local workers and specialists (e.g., plumbers, translators), has maintained a broad definition of what tasks can be offered or hired through the platform (thus helping consumers' innovations) and adopted some innovative skills offered by consumers (e.g., offers to wait in line for others) as standard categories in the platform. To further leverage consumers' improvising actions, TaskRabbit could confer badges to consumers who successfully break role boundaries, encourage consumers to identify the innovators among their cocreation partners, offer alternative profile categories (beyond their general label for service providers [""taskers""]), prompt consumers to teach each other something new while interacting, and encourage them to become local ambassadors.To further support consumers in personalizing their cocreation, TaskRabbit should encourage consumers to engage in creative resourcing by actively prompting them to innovate or highlight the presence of new resources through badges, tags, and curations. For example, taskers, to describe all of their resources, often find workarounds to the limited options available on their platform profiles (e.g., offering American Sign Language as a skill-for-hire to showcase their language fluency). The platform could help consumers share these hacks with others through the TaskRabbit app or create a customizable language tag (rather than tags only for the most popular languages). Furthermore, TaskRabbit could support consumers' repurposing actions. For example, consumers may search for friendship or companionship through the platform, and the firm could create categories of social tasks to reflect these alternative purposes. Overall, by developing business models that support consumer experimentation actions, platform firms in the sharing economy can facilitate consumers' cocreation of experiences that are unique and valuable to participants. These innovations can eventually be picked up by the firm and help it continuously offer novelty to its stakeholders. When Consumer Orchestration Works BestIn the sharing economy, business models vary in terms of how much control they allow consumers to have over their cocreation activities. The decision of how much control to give to consumers is often a strategic one. The platform firm may prefer to keep a tight grip on consumers' interactions as a way of reducing costs, optimizing efficiencies, and enabling preset complementarities among partners. This type of firm-led orchestration of experiences works well in contexts in which experiences are expected to be similar or consistent (e.g., Kickstarter) and/or those in which heterogeneity does not affect the outcome of experiences (e.g., Lending Club). This also works well when sociality is not important (e.g., Zipcar), interpersonal trust is not required (e.g., Quirky), or experimentation is not valued (e.g., Lime). In these cases, the firm-led orchestration of experiences via traditional marketing controls (e.g., standard service quality metrics) should be preferred, as this saves costs and enables the firm to harvest the value of maximizing resource allocation and business scaling.Other platform business models thrive on enabling unique experiences (e.g., Airbnb). Many require high consociality ([34]), as consumers need interaction to cocreate experiences (e.g., Uber); this often demands interpersonal trust (e.g., BlaBlaCar, TaskRabbit) and the alignment of consumers' expectations, actions, and responses (e.g., Tinder). It is for these groups of platform firms that consumer-led orchestration work dominates and the findings of this research are most relevant.Orchestration work, although performed by consumers, can be influenced in important ways by the affordances of the platform. For example, the specific way in which C2C alignment manifests depends on the search filters, algorithms, and peer-to-peer communication tools available on the platform. Orchestration work is also shaped by how the platform firm incentivizes or limits consumers' interactions outside the platform. Importantly, Couchsurfing is a low-intermediation platform ([34]), in that it does not interfere much with what consumers are doing.These findings align with [34] contention that marketers, to effectively hone and market their value propositions, must understand their market stakeholders. We support and extend their claim that each platform type has ""particular forms of value creation that should focus managers' business investment decisions and resource deployment"" ([34], p. 33) by considering how consumer-led value cocreation impacts each type differently. We argue that the platform types with business models that depend on a high degree of consumer interaction, such as Couchsurfing, Airbnb, and the others that [34] call ""forums"" and ""matchmakers,"" are most likely to profit from delivering unique experiences. These platforms should focus on leveraging consumer-led orchestration actions by way of the mechanisms we have outlined. In contrast, platforms with a limited degree of consumer interaction—for example, Kickstarter and other ""enablers"" and ""hubs"" ([34], p. 20)—have less to harvest from consumer-led orchestration. In these cases, platform forms should be selective in terms of how they engage with consumer-led orchestration. These firms should consider whether each source of value creation (i.e., novelty, efficiency, lock-in, and complementarities) they wish to foster is better activated by consumer- or firm-led orchestration. Theoretical Implications Expanding on How Consumer Work Creates Value for FirmsIn line with prior research that examines how consumers cocreate value within a consumer collective (e.g., Schau, Muñiz, and Arnould 2009), we focus on identifying what consumers are doing to create value for themselves, and we see value for the firm as a welcomed, if not desired, outcome of consumer activity. However, the sharing economy's consumer collectives differ from the more communal collective contexts previously explored in the literature, such as online brand communities (Schau, Muñiz, and Arnould 2009) and interest-based communities ([ 7]; [46]). As such, most prior research has examined value cocreation in consumer collectives as an outcome of shared practices within the collective (e.g., [17]; [19]). In contrast, we identify the challenges to cocreation that are intrinsic to sharing platform collectives and map the emergent orchestration actions that consumers enact to surpass the roadblocks in cocreation and to have more valuable experiences. Thus, the orchestration work we identify here does not directly cocreate value. Instead, by overcoming the challenges to cocreation that are characteristic of these sharing platforms, these orchestration actions enable the conditions for the cocreation of value.We also show how by engaging in orchestration work to enable the cocreation of value for themselves, the enterprising consumers ([14]) who cocreate in the sharing economy also support value creation for platform firms. Going beyond prior research, we explain how each orchestration mechanism may lead to multiple sources of value creation that are at the core of platform business models ([56]). This newly identified type of consumer entrepreneurial work advances the understanding of prosumer work (see, e.g., [57]). In orchestration work, platform consumers (both service providers and users) enact forms of governance that are traditionally associated with marketing managers. Through orchestration actions, enterprising consumers take control of the platform's value-creating activities.Consumer orchestration work then explains how it is possible for platforms, such as Couchsurfing, to function well and generate value for millions of heterogeneous participants who are guided by hybrid logics despite maintaining limited control over the quality of service providers and consumers' experiences ([12]). We show how, for example, through trust investment orchestration actions (e.g., revealing additional information, cultivating reviews, scaffolding interactions), consumers complement platform-based mechanisms for reducing risk (e.g., rating systems, insurance for payments or damages) with interpersonal trust. Considering the myriad of ways in which consumers address trust-related roadblocks can extend knowledge on the interdependency of platforms' and consumers' reputations in the sharing economy ([11]).We also note how, in the realm of cocreated experiences in the sharing economy, innovation is emergent ([35]); it comes largely from consumers' activity as they ideate, collaborate, and experiment in the network to develop solutions tailored to their needs ([ 1]). Our findings highlight the serendipitous nature of consumer orchestration, which favors the discovery of value potential in the collective ([31]), in turn bringing novelty to the consumer collective and the platform firm. This way of understanding innovation as an outcome of the orchestration of experiences can address recent calls for research on the specific mechanisms that drive consumer-led innovation in the sharing economy ([12]). In Table 3, we outline directions for future research on various areas connected to value cocreation by consumers.GraphTable 3. Future Research Directions. ConclusionThis article makes important contributions to research in marketing. First, it discusses the challenges that the hybrid logics of the sharing economy raise for consumers who are cocreating experiences. Second, it identifies orchestration as an important form of work conducted by platform consumers (service users and providers) to overcome these challenges. Third, it connects consumer orchestration work to known sources of value for platform firms and provides recommendations for platform firms to leverage the power of orchestration work. Finally, it offers a series of research questions that can inspire marketing researchers to further explore how consumers cocreate in the sharing economy. " 29,How Industries Use Direct-to-Public Persuasion in Policy Conflicts: Asymmetries in Public Voting Responses," Industries use persuasion strategies to gain public support when challenged by activist groups on consumer-relevant issues. This marketing practice, termed ""direct-to-public persuasion,"" has received limited attention in the field, and thus we have little understanding of when such campaigns fail or succeed. This research introduces a theoretically derived and empirically supported framework that draws from multiple areas, including marketing persuasion, political campaign strategy, sociopolitical legitimacy, and perceptual fit, to identify important differences in the effectiveness of these persuasion strategies on attitudes and voting behavior. The multimethod approach includes a field study of ballot measure voting during a national U.S. election and three experimental studies. The findings contribute new knowledge of asymmetries in public response to industry and activist arguments. Stronger arguments from both sides lead to more favorable outcomes, but activist groups benefit most. Suspicion of activist arguments weakens the impact on attitudes and voting; industry argument suspicion has limited impact, though it does increase the likelihood of voter switching. A financial argumentation strategy works best for the industry side, while societal argumentation is more effective for the activist side. The insights offer guidance for industries and activist groups as argument strategy success is contingent on the side that uses it.","Industries often try to persuade the public at large to support their positions on prominent issues that are consequential for consumers and industry members. We term this phenomenon ""direct-to-public persuasion,"" a marketing-driven political influence strategy used to convince the public that the industry is on the correct side of an issue. Industries ""talk"" to the public not only to sustain or gain advantageous conditions, such as preventing policy actions viewed as costly or restrictive, but also to align the public with practices that further industry objectives.Industries use direct-to-public persuasion to influence public voting on ballot measures in state and local referendums, public opinion in advance of legislative policy maker decisions, and ongoing public attitudes toward the industry. We focus on conflict scenarios in which industries are challenged by activist groups—collectives that advocate for the public interest—in ballot measures (e.g., [67]). Such conflicts are commonly high-profile and a strategic priority for industries, which spend substantial funds to secure favorable outcomes and protect industry practices ([42]; [59]). The outcomes often have meaningful consequences for the public, including consumers, but the question of what drives voters to support one side or the other remains unanswered.Table 1 presents examples of how industries and their activist opponents use direct-to-public persuasion to influence the public and gain support. The examples represent diverse issues, including food labeling, recycling, pharmaceutical drug pricing, and tobacco taxes. In the scenarios we examine in this article, industries defend the policy status quo to resist changing disputed practices (e.g., price limits on pharmaceuticals). However, in other settings, industries fight the status quo to expand or develop new markets.[ 5]GraphTable 1. Examples of Direct-to-Public Persuasion in Issue Referendums. 1 Notes: Information on ballot measures retrieved from https://ballotpedia.org/Ballot_Measures_overview.Industries have resource advantages relative to activist opponents that suggest they should win policy issue battles, and they often do ([56]; [65]). Because industries are often in the advantageous role of defending the policy status quo, they can leverage the uncertainty of any policy change, whereas activist groups often have the more difficult task of convincing the public that the status quo is failing ([19]). Despite their advantages, industries do not always prevail, which may be due to an underlying predisposition of the public to be more skeptical of the industry and its motives ([ 8]; [45]; [62]). The fact that industries do frequently win suggests that they succeed in overcoming the public's skepticism with effective persuasion arguments ([44]). Our research examines the dynamics of competing campaigns to better understand and explain voting outcomes.The industry-level practice of direct-to-public persuasion has received limited attention in the marketing field, and we know little about persuasion involving political campaigns with competing positions on an issue. The marketing literature has examined consumer responses to persuasion attempts in a variety of important contexts, and advertising research has examined aspects of argument effectiveness (e.g., [46]; [66]), but political arguments are believed to differ from commercial arguments ([27]). Political marketing research has largely focused on candidate elections rather than election voting on policy issues (see [35]; [38]), and the lobbying literature addresses influencing policy makers rather than influencing the public (see [43]; [67]). Further, although studies show that voters often switch their support during campaigns, and even a nominal shift can change the outcome ([44]; [50]), knowledge of what causes the switching is limited.To expand knowledge of this understudied topic, we develop a framework that draws from multiple research areas, including marketing persuasion, political campaign strategy, sociopolitical legitimacy, and perceptual fit. We apply legitimacy theory to explain our predicted asymmetry of argument effectiveness across the competing sides and draw on perceptual fit theory to explain the efficacy of specific argumentation strategies on the public's attitudes and voting behavior. We use a multimethod approach that includes a field study of ballot measure voting on two policy issues during a national U.S. election and three experimental studies involving additional issues to test our ideas. The policy issues included prescription drug pricing, tobacco product taxes, renewable energy standards, and container recycling laws.Our findings offer insight into how the public's attitudes and voting intentions change during the campaign when they are exposed to competing arguments from industry and activist groups. First, we uncover important asymmetries that should be considered in understanding and in using direct-to-public persuasion strategies. Specifically, stronger arguments from both industry and activist groups lead to more favorable attitudes and voting outcomes, but activist groups benefit more from stronger arguments. Greater argument suspicion weakens the impact on attitudes and voting, but primarily for activist groups. Therefore, overall, voters' evaluations of activist-side arguments have a greater impact than industry-side arguments on outcomes. Second, an exploration of vote switching found that industry argument suspicion has limited impact, though it does increase the likelihood of voter switching, particularly for voters who switch their support to the activist side. Finally, using follow-up experiments, we show that a financially focused argumentation strategy works best for the industry side, whereas a societally focused strategy is more effective for the activist side.We contribute to marketing research with new knowledge of a marketing practice that influences voters and critical political outcomes. We add to the scope of persuasion research with insight into how voters respond to industry versus activist political campaigns, finding novel evidence of an asymmetric public response, where voter judgments of competing arguments—and the degree to which those arguments are strong or suspicious—help predict the relative impact of each side's campaign. We use sociopolitical legitimacy theory in a new way, proposing that an industry-activist legitimacy gap helps explain why industry argument strength has less impact, and apply persuasion theory to explain why voters give greater weight to suspicion of activist arguments. Our pre- and postelection recontact field study design captures individual voter switching, which is not commonly examined, and provides a rare indication that determinants differ for voter segments that flipped their allegiance from one side to the other. Finally, we offer the first known concrete guidance for the use of direct-to-public persuasion strategies with recommendations that differ for industries and activist groups, as we find that a given strategy's success is contingent on the side that uses it. Direct-to-Public Persuasion Model and Hypothesis DevelopmentFigure 1 depicts the conceptual relationships we investigate. Our model predicts that the public's evaluations of industry- and activist-side arguments, in terms of argument strength and argument suspicion, play a central role in predicting outcomes including attitudes toward the issue, voting on the issue, and vote switching. These evaluations depend on each side's argumentation strategy. We begin by describing the two competing sides in the issue conflict scenarios we study and key distinctions between them, and then we develop the hypothesized asymmetric effects of argument strength and suspicion on the voting outcomes.Graph: Figure 1. Conceptual model. Industry and Activist Conflicts over IssuesWe define ""competing sides on the issue"" as the competitors in a policy issue conflict and focus on industry groups and activist groups, traditional policy adversaries ([ 8]). While industries comprise firms that produce a certain class of goods or services, public interest activist groups are political constituents working in a range of policy areas who claim to represent the public or collective good (e.g., [57]; [67]). Evidence suggests that the public responds differently to the industry and activist sides because of enduring skepticism about the undue influence of business interests ([25]; [49]. While attitudes toward individual industries vary, public favorability ratings of business have trended downward for decades and eroded further since the 2008 financial crisis ([ 1]; [68]).A legitimacy theory perspective indicates that response to direct-to-public persuasion from the industry and activist sides should be asymmetric due to a legitimacy gap caused by the belief that industry-serving interests drive industry-side motives whereas commitment to the public good drives activist-side motives ([56]). Normative and sociopolitical legitimacy underlie the public's judgment of whether an industry practice is acceptable—meeting cultural and political norms—or should be sanctioned (e.g., [40]; [61]). Legitimacy judgments are likely to discourage many voters from initially supporting the industry side, but election polling and outcomes suggest that voters come to judge the industry more favorably during the course of a campaign. For example, in recent ballot measures that sought genetically modified organism labeling on food products (Oregon Measure 92 in 2014), regulations on dialysis clinic pricing (California Proposition 8 in 2018), and expansion of bottle deposit laws (Massachusetts Question 2 in 2014), the majority initially supported the activist side but switched to support the industry position, allowing the industry to win the vote (see https://ballotpedia.org/List_of_ballot_measures_by_year). The Influence of Competing Persuasion Campaign ArgumentsArgumentation is defined as a strategic approach used to justify a particular policy or political position and promote or challenge its implications ([ 6]; [20]). Effective argumentation is critical for industries battling controversy and for activist groups that confront them, as both strive to legitimize their positions ([21]; [39]). We explore the effectiveness of each side's argumentation on two key dimensions: argument strength and argument suspicion. Research on argument strength, including studies in competitive political contexts (e.g., [14]), has not concurrently examined the effects of argument suspicion.We define ""argument strength"" as perceived persuasiveness determined by an individual's cognitive response, either favorable or unfavorable, to an argument ([ 4]; [71]). Empirical findings show that strong arguments consistently shift attitudes and beliefs but to varying degrees (e.g., [ 3]). Although argument strength has had limited study in real-world scenarios of competing arguments over time, some research reports that in the presence of a strong argument from one side, a weaker argument from the opposing side is viewed even more negatively and can actually backfire ([14]). As baseline predictions, we hypothesize: H1: Industry argument strength increases (a) favorable attitudes toward and (b) voting in favor of the industry side on the issue. H2: Activist argument strength increases (a) favorable attitudes toward and (b) voting in favor of the activist side on the issue.We define ""argument suspicion"" as the perception that an argument is implausible or has a hidden intent (e.g., [13]; [24]). Although there is far less theoretical and empirical knowledge on the influence of argument suspicion than argument strength, marketing research on persuasion knowledge and skepticism toward advertising offers rich evidence that suspicion-driven responses to persuasion can have significant negative effects on attitudes and behaviors.Consumers draw on persuasion knowledge, beliefs about how marketers influence consumers, to determine whether something is a persuasion attempt (e.g., [13]). In our research scenario, arguments have clear intent to persuade to vote no or vote yes, but whether the arguments' tactics are viewed as appropriate impacts their effectiveness (e.g., [34]). In some cases, voters may infer finer-level motives in an argument's tactics that trigger suspicion (e.g., Is the ""vote no"" side using arguments that demonize the ""vote yes"" side because it lacks more valid arguments?; [37]). Research on skepticism toward advertising messages has examined dispositional ad skepticism (e.g., [48]) as well as situational skepticism (e.g., [22]). Marketing studies suggest that when voters respond to an industry's arguments with skepticism, they are likely to scrutinize its underlying intentions, suspicious that they are deceptive as well as self-serving (see, e.g., [22]).While this suggests that the public is inclined to view industry arguments as suspicious, other research suggests that activist arguments can also face public doubt, such as when they appear to exaggerate the problems and risks related to contested issues ([ 7]; [62]). As such, we expect voter evaluations of argument suspicion to be relevant for both sides on the issue. We predict the following: H3: Industry argument suspicion decreases (a) favorable attitudes toward and (b) voting in favor of the industry side on the issue. H4: Activist argument suspicion decreases (a) favorable attitudes toward and (b) voting in favor of the activist side on the issue. Asymmetric Response to Competing Industry and Activist Arguments The impact of argument strengthWhile we predict that argument strength will have a favorable effect for both the industry and activist sides, there is some evidence suggesting that effects are not equally favorable. We suggest, based on the difference in sociopolitical legitimacy ([ 9]; [61]), that strong industry arguments will have a weaker effect relative to strong activist arguments. Due to their negative views of industry practices, voters' legitimacy judgments may trigger doubt of the underlying intent of industry arguments. As a result, voters may not be as influenced even by strong arguments to support the industry's side as by strong arguments to support the activist side. For example, voters assessing a proposition to cap drug prices (e.g., Proposition 61 in Table 1) may mistrust the motives underlying pharmaceutical industry pricing practices, which could diminish the impact of a strong industry argument. In contrast, because voters are likely to have more positive legitimacy judgments of activist groups (e.g., the AIDS Healthcare Association, which works to make life-saving drugs more affordable), the strength of their arguments will not be diminished. This type of voter response is consistent with research showing that when a prior ad triggers doubts about that ad's intent, the effects of argument strength in subsequent ads are diminished ([16]). We hypothesize: H5: Industry argument strength has a weaker positive effect than activist argument strength on (a) attitudes toward the issue and (b) voting on the issue. The impact of argument suspicionVoters' responses to influence attempts reflect their persuasion beliefs, and they are likely to be far more guarded against industry arguments than activist arguments (see [37]). This initial disadvantage for the industry side may ultimately offer it an opportunity to use campaign tactics that lead voters to put more weight on activist argument suspicion and/or less weight on industry argument suspicion.A method used by industries to increase voter suspicion of activist opponents is to intensify the public's doubt about activist-supported policy changes. Industries often argue that activist-side policy positions are impractical, ill-conceived, unrealistic, and costly ([ 6]; [31]). For instance, industry arguments might warn of hidden complexities or expenses, implying that activist arguments are not as genuine and aboveboard as they may seem ([19]). The tactic of using cogent, rational challenges to activist arguments is likely viewed as credible, as it seems cautious and shows due diligence. With this approach, the industry can heighten voters' formerly low persuasion knowledge of the activist side, increasing the weight of activist argument suspicion in voting decisions. This aligns with findings that previously trusted persuasion claims are further scrutinized when they are revealed to have hidden intentions (e.g., [13]), particularly in competitive contexts such as ours (e.g., [14]).[44] study of a 2012 California ballot measure (Proposition 37) to require labeling genetically engineered foods supports this view. They found that industry advertising helped flip voter support for the activist side (around 70% less than four weeks prior to the election) to majority support for the industry. Industry advertising focused on loopholes that would make implementation difficult and costly, while activist advertising (which lost traction over time) focused on the ruthlessness and deceit of the large industries involved. Although research in ballot measure, referendum, and other issue-focused election scenarios is limited, similar tactics are described in other sources (e.g., [33]; [53]). Following this logic and evidence, we hypothesize: H6: Industry argument suspicion has a weaker negative effect relative to activist argument suspicion on (a) attitudes toward the issue and (b) voting on the issue. Exploring Responses of Vote-Switching SegmentsDirect-to-public persuasion is most effective when it actually switches voter support from one side to the other. While brand switching is well examined, less is known about vote switching, when persuasion campaigns are short in duration and have a common deadline ([58]). An analysis of vote switching is needed to show whether voting outcomes are due to each side's arguments solidifying voters' initial support or causing them to switch their support over the course of a campaign ([38]).There is limited guidance on whether the response asymmetry our model predicts would hold for voters who switched their support compared with those who did not or whether the drivers of switching could differ for those initially supporting the industry side compared with those initially supporting the activist side. Swings in public support are well documented by public opinion research, yet there is limited knowledge of how competing campaign arguments motivate voters to switch allegiance. Because research provides little insight on which to base predictions, we do not formally hypothesize effects on switching behavior but rather allow the empirical results to address the knowledge gaps. Field Study of Ballot MeasuresTo test our hypotheses, we conducted a large field study examining attitudes toward and voting on two California ballot measures involving distinct issues from different industries: Proposition 56, which proposed a tobacco tax increase, and Proposition 61, which proposed price limits on prescription drugs purchased for state programs (for examples of campaign media, see Web Appendix W1). These propositions featured highly salient, consumer-relevant issues in which the industries invested significantly in direct-to-public persuasion campaigns to defeat the measures. By investigating different issues and industries, we reduce the potential that our analyses capture idiosyncratic factors particular to one issue or one industry. MethodologyWe examined the impact of industry and activist arguments on attitudes and voting using survey data we collected using a national consumer research panel of California voters in two waves: five weeks before (measuring respondent characteristics, preelection attitudes, and voting intentions) and one week after (measuring argument strength, argument suspicion, postelection attitudes, and self-reported voting) the November 2016 national election. We collected data for the same respondents in both waves to analyze the extent to which industry- and activist-side persuasion campaigns shifted individual voters' attitudes and voting. SampleWe recruited respondents through Qualtrics, an online market research panel aggregator whose quality certification includes checking each person's IP address to exclude duplication and replacing those who finish the survey in less than one-third of the average completion time. Respondents were registered voters in California who indicated ""I will vote"" in the election in a screening question at the beginning of the preelection survey. After the election, we invited all 1,806 respondents of the preelection survey to answer the postelection survey. Of the 904 postelection survey respondents who voted in the election,[ 6] 58.4% were female, the mean age was 51 years old, 92.1% had some college education or higher, and 70.7% reported their socioeconomic status (SES) as middle to upper class. MeasuresIn the preelection survey, we captured preelection attitudes toward the issue in the context of the drug price and tobacco tax propositions using a three-item (""wrong/right,"" ""harmful/beneficial,"" and ""unacceptable/acceptable""), seven-point, previously validated scale and voting intention on the issue for both propositions as a binary variable (0 = intend to vote ""no,"" in favor of the industry side; 1 = intend to vote ""yes,"" in favor of the activist side). In addition, we measured several individual-level characteristics, including political affiliation, need for orientation, and demographic characteristics (age, gender, education, and SES).In the postelection survey, we measured our two dependent variables, attitudes toward the issue and voting on the issue, using the same multi-item scale used to capture preelection attitudes and a binary variable (0 = voted ""no,"" 1 = voted ""yes""), respectively. Respondents were then presented with two principal arguments from both the industry and activist sides for both propositions. To emphasize external validity, we identified the principal arguments from the California Official Voter Information Guide published by the office of the [12], which includes a set of Official Arguments formally submitted by each side (see Web Appendix W2). Respondents were asked to indicate their perceptions of argument strength and argument suspicion for each pair of industry arguments and activist arguments. We measured the two variables with two-item, seven-point, previously validated scales measuring the extent to which each of the arguments was convincing and aroused suspicion.[ 7] Finally, we measured familiarity with the arguments. For all multi-item scales, the mean of the items was used in the model estimation. Table 2 presents the descriptive statistics for all variables, and measurement details are provided in Web Appendix W2.GraphTable 2. Descriptive Statistics for Field Study Variables. 2 Notes: Correlation coefficients below diagonal relate to drug price proposition (>|.067| are significant at p < .05; N = 851). Correlation coefficients above diagonal relate to tobacco tax proposition (>|.066| are significant at p < .05; N = 871).SES = socioeconomic status. ModelWe tested our hypotheses by estimating attitudes toward and voting on the issue as a function of argument strength and argument suspicion. For postelection attitudes toward the issue (Attitudest,i), we used an ordinary least squares (OLS) approach, specified as follows: Attitudest,i=β0+β1Industry_Argument_Strengtht,i+β2Activist_Argument_Strengtht,i+β3Industry_Argument_Suspiciont,i+β4Activist_Argument_Suspiciont,i+β5Attitudest−1,i+β6Familiar_Industryt,i+β7Familiar_Activistt,i+β8Political_Affiliationi+β9Orientationi+β10Agei+β11Genderi+β12Educationi+β13SESi+εi, Graphwhere the key predictor variables are industry argument strength (Industry_Argument_Strengtht,i), activist argument strength (Activist_Argument_Strengtht,i), industry argument suspicion (Industry_Argument_Suspiciont,i), and activist argument suspicion (Activist_Argument_Suspiciont,i). Covariates include preelection attitudes toward the issue (Attitudest − 1,i), familiarity with industry and activist arguments (Familiar_Industryt,i, Familiar_Activistt,i), political affiliation (Political_Affiliationi), need for orientation (Orientationi), age (Agei), gender (Genderi), education (Educationi), and socioeconomic status (SESi). For our voting on the issue outcome (Votet,i), we used a logit model with the same predictors and covariates as the attitudes toward the issue model, substituting preelection voting intentions (Votet − 1,i) in place of preelection attitudes.Our analysis was subject to several forms of bias, which we wanted to resolve. First, because we measured our dependent variables and key predictor variables using the same instrument, we adopted [41] approach to test for common method variance using a method factor capturing need for orientation on an unrelated ballot measure included in the survey instrument. Correlation coefficients corrected for common method variance indicate that this does not significantly bias our data (see Web Appendix W3).Second, we addressed endogeneity due to either reverse causality between argument strength and attitudes toward the issue or omitted variables using two-stage least squares estimation and comparing the estimated coefficients with the OLS estimated coefficients. We specified the first-stage equation to include a set of exogenous variables (the covariates from the Attitudest,i model) plus an instrumental variable, Reactancei, capturing individual i's resistance to a perceived threat to free choice or behavior. The instrument was relevant (i.e., correlated with argument strength) and met the exclusion criterion (i.e., uncorrelated with the error term of the Attitudest,i equation). In the second-stage equation, the estimated residuals from the first-stage regression were included in the Attitudest,i model to control for the endogenous regressors. Our test indicates that endogeneity does not bias the OLS coefficients (for details regarding the appropriateness of our instrument and model results, see Web Appendix W4).Third, because a subset of respondents from the preelection survey opted to complete the postelection survey, we addressed selection bias using [30] two-step estimation of the effect of argument strength on attitudes toward and voting on the issue. In the first-stage selection equation, Votedt,i (binary variable indicating whether or not the respondent voted on the proposition in the election) is regressed on our covariates plus the conceptually unrelated variable Reactancei. The second-stage outcome equations for attitudes and voting were estimated using the Attitudest,i and Votet,i model specifications, respectively, conditioned on whether the respondent voted on the proposition in the election. From the estimated coefficients for the second-stage model, including the inverse Mills ratio, we conclude that selection bias did not adversely affect model estimation (see Web Appendix W5). Results The Influence of Competing Persuasion Campaign ArgumentsA model-free examination of the data offers preliminary evidence of our predicted relationships (see Web Appendix W6). Table 3 contains the results related to our hypothesized effects. Note that for our analysis, as industry is opposed to the proposition, negative coefficients indicate favorable attitudes toward and voting in favor of the industry side. We find consistent support for H1 and H2. Stronger industry arguments led to more favorable attitudes toward the industry side on both the drug price (βIndustry_Argument_Strength = −.178, p < .01) and tobacco tax (βIndustry_Argument_Strength = −.128, p < .01) propositions and increased the likelihood of voting in favor of the industry side on the drug price (βIndustry_Argument_Strength = −.623, p < .01; 65.1% likelihood of voting no[ 8]) and tobacco tax (βIndustry_Argument_Strength = −.476, p < .01; 61.7% likelihood of voting no) propositions. Similarly, stronger activist arguments led to more favorable attitudes toward this side for the drug price (βActivist_Argument_Strength = .630, p < .01) and tobacco tax (βActivist_Argument_Strength = .471, p < .01) propositions and increased the likelihood of voting in favor of this side for the drug price (βActivist_Argument_Strength = 1.300, p < .01; 78.6% likelihood of voting yes) and tobacco tax (βActivist_Argument_Strength = 1.206, p < .01; 77.0% likelihood of voting yes) propositions.GraphTable 3. Impact of Industry and Activist Argument Strength and Suspicion on Attitudes Toward and Voting on the Issue. 3 *p < .10.4 **p < .05.5 ***p < .01.6 Notes: One-tailed tests of significance. AIC = Akaike information criterion. SES = socioeconomic status. For attitudes toward the issue, negative coefficients indicate favorable attitudes toward the industry side on the issue (as industry opposes the proposed policy); positive coefficients indicate favorable attitudes toward the activist side on the issue (as activist side supports the proposed policy). For voting on the issue, negative coefficients indicate voting in favor of the industry side on the issue (as industry opposes the proposed policy); positive coefficients indicate voting in favor of the activist side on the issue (as the activist side supports the proposed policy).Our results offer mixed support for H3, as suspicion of industry arguments had little impact on voting outcomes. It had no effect on attitudes toward either proposition, contrary to H3a. It significantly decreased the likelihood of voting in favor of the industry side for the drug price proposition (βIndustry_Argument_Suspicion = .194, p < .05; 45.2% likelihood of voting no), but not for the tobacco tax proposition, partially supporting H3b. By contrast, suspicion of activist arguments played a consistent role, fully supporting H4, resulting in less favorable attitudes toward that side for the drug price (βActivist_Argument_Suspicion = −.172, p < .01) and tobacco tax (βActivist_Argument_Suspicion = −.120, p < .01) propositions and decreased likelihood of voting in favor of that side for the drug price (βActivist_Argument_Suspicion = −.391, p < .01; 40.3% likelihood of voting yes on the proposition) and tobacco tax (βActivist_Argument_Suspicion = −.283, p < .01; 43.0% likelihood of voting yes) propositions. Asymmetric Response to Competing Industry and Activist Arguments The impact of argument strengthTo compare the relative impact of industry and activist argument strength in predicting our key outcomes, we used the delta method to test the equality of the estimated coefficients for the two variables ([28]). Specifically, we calculated t = (βIndustry_Argument_Strength − βActivist_Argument_Strength)/SE, where βIndustry_Argument_Strength and βActivist_Argument_Strength are the absolute value of the two parameter estimates and SE is the standard error of their difference calculated as Sqrt[Var(βIndustry_Argument_Strength) + Var(βActivist_Argument _Strength) − 2 × Cov(βIndustry_Argument_Strength, βActivist_Argument_Strength)]. The results provide full support for H5, indicating that activist argument strength has a significantly greater impact than industry argument strength on attitudes toward the drug price (t = −4.077, p < .01) and tobacco tax (t = −2.821, p < .01) propositions and on voting for the drug price (t = −3.551, p < .01) and tobacco tax (t = −3.400, p < .01) propositions. This is consistent with a comparison of effect sizes, which indicates that activist argument strength has a strong effect on attitudes toward both the drug price ( ηp2  = .285) and tobacco tax ( ηp2  = .202) propositions, while industry argument strength has a moderate-small effect for the drug price ( ηp2  = .035) and tobacco tax ( ηp2  = .026) propositions.[ 9] The impact of argument suspicionUsing the delta method to compare the relative impact of industry and activist argument suspicion in predicting attitudes and voting revealed that activist argument suspicion has a significantly greater impact than industry argument suspicion on attitudes toward the drug price (t = −2.618, p < .01) and tobacco tax (t = −1.969, p < .05) propositions and on voting for the drug price (t = −2.249, p < .05) and tobacco tax (t = −2.192, p < .05) propositions, in support of H6. A comparison of effect sizes reflects this difference, such that activist argument suspicion has a moderate-small effect on attitudes toward the issue for both the drug price ( ηp2  = .037) and tobacco tax ( ηp2  = .026) propositions, but industry argument suspicion has a negligible effect for the drug price ( ηp2  = .000) and tobacco tax ( ηp2  = .002) propositions. Effect sizes for all parameters and overall model statistics are reported in Table 3. Robustness ChecksAlthough our survey respondents' characteristics generally align with the demographics of California voters in the 2016 election based on U.S. Census Bureau data in terms of gender, age, and income, our sample included higher proportions of individuals with higher education levels and whites/Caucasians and lower proportions of Hispanics (see Web Appendix W7). To help ensure that our results are representative of California voters, we performed a robustness check in which we weighted observations using an iterative proportional fitting procedure, which takes the marginal distributions of demographic variables from the California voter population and returns weights such that the marginal distributions of the demographic variables in the weighted survey data match those in the population ([69]). Compared with results for the unweighted models, our analysis finds consistent effects for 15 of the 16 total hypothesized effects of the impact of argument strength and argument suspicion on attitudes toward and voting on the issue for the two propositions (see Web Appendix W8). Exploration of Vote Switching SegmentsOf the two ballot measures we study, the drug price proposition experienced significant swings in public support. Political polling in California leading up to the election indicated that 73% of registered voters supported the proposition in late July ([63]), but this decreased to 66% in mid-September (USC Dornslife/LA Times) and further to 51% in mid-October (Hoover Institute/YouGov). Following intensive pharmaceutical industry media spending in the six weeks before the election, the proposition was defeated in the November election with 48.6% of support (all polling data retrieved from https://ballotpedia.org/California%5fProposition%5f61,%5fDrug%5fPrice%5fStandards%5f(2016)).[10] Such a swing in support did not occur for the tobacco tax proposition.We considered two scenarios. First, we focused on voters who intended to vote in favor of the activist side in the preelection survey (i.e., would vote yes on the proposition) and measured our dependent variable by identifying those who actually voted in favor of that side (voted yes) versus those who switched and actually voted in favor of the industry side (voted no) as measured in the postelection survey (1 = switched vote, 0 = did not switch vote). Second, we focused on those who intended to vote in favor of the industry side and measured the dependent variable by identifying those who actually voted in favor of the industry side versus those who switched and voted in favor of the activist side.As Table 4 shows, argument strength and argument suspicion play important roles in vote switching (all results apply to both the drug price and tobacco tax propositions). Strong industry (activist) arguments significantly increased (decreased) the likelihood of voters switching support from the activist side to the industry side and decreased (increased) the likelihood of switching support from the industry side to the activist side. Consistent with our previous analyses, activist argument strength had a greater impact than industry argument strength in both scenarios. The results related to argument suspicion reveal a notable effect that may have been masked in our previous analyses. Consistent with our previous findings, suspicion of activist arguments significantly increased (decreased) voter switching to the industry (activist) side. However, in contrast with our previous findings, suspicion of industry arguments significantly increased the likelihood that voters who initially supported the industry switched their support to the activist side. This distinctive finding is bolstered by a test of equality on the impact of argument suspicion for the opposing sides, which indicates that suspicion of activist arguments does not play a significantly greater role than suspicion of industry arguments in this scenario.GraphTable 4. Impact of Industry and Activist Argument Strength and Suspicion on Vote Switching. 7 *p < .10.8 **p < .05.9 ***p < .01.10 Notes: One-tailed tests of significance. AIC = Akaike information criterion. SES = socioeconomic status. For attitudes toward the issue, negative coefficients indicate favorable attitudes toward the industry side on the issue (as industry opposes the proposed policy); positive coefficients indicate favorable attitudes toward the activist side on the issue (as activist side supports the proposed policy). For voting on the issue, negative coefficients indicate voting in favor of the industry side on the issue (as industry opposes the proposed policy); positive coefficients indicate voting in favor of the activist side on the issue (as the activist side supports the proposed policy).Our field study findings affirm the relationships predicted in our framework and hypotheses. In the next section, we develop hypotheses predicting how two distinctive and prominent argumentation strategies, one focused on financial factors and one focused on societal factors, vary in effectiveness for the industry and activist sides. We then test our predictions using three experimental studies. Comparing Argumentation Strategy Effectiveness for the Industry and Activist SidesPrior research on perceptual fit suggests that argument effectiveness would vary depending on fit with the persuader (e.g., [60]). The positive effects of high fit between a firm or a brand and entities with which it is associated, such as sponsorships and cause-related marketing, are well established ([54]; [60]). Despite this, [ 6] document that opposing sides, including industry and activist groups, tend to use the same few strategies in the same policy battle, indicating that the fit between the persuader and the argument appears to be often overlooked. This raises an important question: Would a more focused approach be more effective and, if so, how should it be chosen?To address this question, we examine argumentation strategies consistent with the image of the two sides in the conflicts we study. Financial argumentation is based on factors related to imposing or reducing financial benefits or costs, whereas societal argumentation is based on (nonfinancial) factors related to inhibiting or promoting shared societal goals or values. With financial argumentation, an industry may claim that there will be onerous financial costs for the public if current policy changes, whereas an activist group may argue that current policy needlessly burdens taxpayers ([19]; [67]). Using societal argumentation, an industry may claim that a policy change would weaken consumer access to important products or services, whereas an activist group may argue that current policy serves only a privileged few ([57]).Fit between an organization and an argument is high when the two are perceived as congruent because they share similar intangible associations ([60]). Argumentation based on financial factors is congruent with how industries are viewed, as an industry's image is characterized by economic and operational aspects ([10]). Analysts' reports profile industries in terms of financial and market performance and industry actions directly affect the economic status of consumers (e.g., [ 2]). Because financial argumentation is more congruent with public expectations of industry, this strategy is a better fit with the industry side and will lead to more effective persuasion ([54]). As such, we predict that financial argumentation will be perceived as stronger and less suspicious when used by the industry side.In contrast, argumentation based on societal factors is congruent with the image of activist groups, which generally are perceived as organizations that represent the collective good for citizens ([59]). These organizations build consensus on higher-level principles and fundamental values such as social justice ([ 7]; [56]). Because the activist side is focused on promoting shared societal goals or values, its image is more congruent with societal argumentation. As such, we predict that societal argumentation will be perceived as stronger and less suspicious when used by the activist side.Although empirical evidence to inform our predictions is limited, research on organizational legitimacy provides some conceptual support by suggesting that industries battling to control policy most often assume a market and economic orientation, expressed by referencing price, competitiveness, and efficiency, whereas groups representing the public interest often assume a civic orientation, expressed by referencing collective interest, solidarity, and integrity ([10]). In summary, we predict: H7: The effect of argumentation strategy on argument strength and argument suspicion is moderated by the competing side on the issue, such that For the industry side, financial argumentation leads to higher argument strength and lower argument suspicion than societal argumentation. For the activist side, societal argumentation leads to higher argument strength and lower argument suspicion than financial argumentation. For financial argumentation, use by the industry side leads to higher argument strength and lower argument suspicion than use by the activist side. For societal argumentation, use by the activist side leads to higher argument strength and lower argument suspicion than use by the industry side.Building on our previous predictions that argument strength and argument suspicion impact attitudes toward and voting on the issue, we expect these evaluations to mediate the effect of an argumentation strategy on voting outcomes. Specifically: H8: The mediating role of argument strength and argument suspicion depends on the competing side on the issue, such that For the industry side, financial (vs. societal) argumentation increases argument strength and decreases argument suspicion, which then increases favorable attitudes toward and voting in favor of the industry side on the issue. For the activist side, societal (vs. financial) argumentation increases argument strength and decreases argument suspicion, which then increases favorable attitudes towards and voting in favor of the activist side on the issue. Experimental Studies of Argumentation Strategy EffectsWe test H7 and H8 in three experimental studies on different issues addressed in recent ballot measures (see Table 1): pharmaceutical drug price standards, which aligns with one of our field study contexts; recyclable bottle deposits; and renewable energy standards. In a controlled, randomized setting, we exposed participants to two opposing messages (one from each side on the issue) that used either financial or societal argumentation strategies, thus separating the effects of argumentation strategy from those of frequency of exposure ([14]). Experimental Study 1 MethodologyWe used a mixed experimental design with two manipulations administered at two points in time within the same survey. Participants were first introduced to a hypothetical scenario in which the residents of a U.S. state are scheduled to vote on a proposition to adopt statewide pharmaceutical drug price standards and were sequentially shown two messages related to the proposition. First, participants saw one of four messages in a 2 (argumentation strategy: financial vs. societal) × 2 (side on the issue: industry vs. activist) between-subjects experimental design (time 1). Next, in the same survey, we manipulated argumentation strategy within subject; depending on which argumentation strategy participants saw at time 1, they saw one of the two strategies (financial or societal) for the opposing side (time 2). In each condition, participants saw a different combination of arguments from each side (both financial; both societal; or one of each, in a different order), enabling us to examine perceptions of argument strength and suspicion of each strategy in the presence of opposing arguments.In the introduction, participants were told that Proposition 25 asks voters to decide whether their state should adopt standards to restrict the amount that some state agencies and programs pay for selected prescription drugs (for stimuli, see Web Appendix W9). We then introduced each message one at a time, indicating that it is a message designed by supporters (opponents) of Proposition 25 to persuade voters to vote yes (no) and support (defeat) the proposition. We derived the arguments used in our manipulations from the official arguments in the California Official Voter Information Guide for Proposition 61 (Drug Price Standards, November 8, 2016).[11]After participants viewed the two arguments, they indicated their attitudes toward the proposition and how likely they were to vote yes or no in the referendum (1 = ""I will definitely vote NO and oppose this proposition,"" and 7 = ""I will definitely vote YES and support this proposition""). Next, they saw the message viewed at time 1 and evaluated its argument strength and suspicion. After this, they undertook the same evaluations for the message viewed at time 2. We then assessed political affiliation, need for orientation, age, gender, education, and SES. Detailed descriptions of each of the measures appear in Web Appendix W10.To enhance external validity, we recruited a nationally representative sample of registered voters. We partnered with Qualtrics to recruit 659 registered voters (49.68% female; Mage = 52.05 years; age range: 18–91 years). Qualtrics enforced quotas so that the sample was proportional to the U.S. population in terms of region, gender, age, household income, education, and ethnicity based on census data. Because the issue was voted on in California in 2016 and Ohio in 2017, we dropped 33 participants who indicated residency in these states, which left a sample of 626 participants for analyses. We conducted a pretest to confirm that our argumentation strategy manipulation was successful (for details, see Web Appendix W11). ResultsGiven the within-subject nature of our design, in which we measure strength and suspicion of the second argument after participants evaluate the first argument, carryover and anchoring effects inherent to the design limit the interpretation of the second message evaluation ([51]). Thus, to test the hypothesized effects in the presence of arguments from both sides, while ensuring that the argument strength and suspicion measures are not subject to bias, we use participants' evaluations of the first argument and control for the argument evaluated second.We used PROCESS Model 58 ([29]) to test our full conceptual model (see Table 5). An OLS regression on argument strength, with argumentation strategy (1 = financial, 0 = societal), side on the issue (1 = industry, 0 = activist), and their interaction as predictors. Covariates were individual-level characteristics and the argumentation strategy participants saw at time 2. The analysis revealed significant negative main effects of argumentation strategy (bfinancial = −.529, p < .01) and side on the issue (bindustry = −.607, p < .01) and the predicted interaction effect (bfinancial × industry = .841, p < .01). A similar regression on argument suspicion revealed significant positive main effects of argumentation strategy (bfinancial = .384, p <= .05) and side on the issue (bindustry = .627, p < .01) and the predicted interaction effect (bfinancial × industry = −.744, p < .01).GraphTable 5. Response to Argumentation Strategies from Competing Sides: Drug Price Standards Proposition. 11 *p < .10.12 **p < .05.13 ***p < .01.14 Notes: Results from PROCESS Model 58, which includes need for orientation, political affiliation, age, gender (male), education, SES, and argumentation strategy at time 2 (0−1) as covariates; 95% confidence intervals reported for conditional indirect effects; n = 622 for these analyses due to missing values on age and SES covariates.Tests of the simple effects of argumentation strategy for each side on the issue showed that for the industry side, financial argumentation led to marginally significantly higher argument strength (bfinancial = .312, p < .10) and significantly lower argument suspicion (bfinancial = −.360, p < .05), as compared with societal argumentation, in support of H7a. For the activist side, societal argumentation led to significantly higher argument strength (bfinancial = −.529, p < .01) and lower argument suspicion (bfinancial = .384, p < .05), as compared with financial argumentation, supporting H7b (negative coefficients indicate enhanced effects and positive coefficients indicate diminished effects because side on the issue is coded as industry = 1 and activist = 0).Tests of the simple effects of side on the issue for each argumentation strategy revealed that, for financial argumentation, use by the industry side was not significantly different than use by the activist side for both argument strength (bindustry = .234, p > .10) and argument suspicion (bindustry = −.117, p > .10), contrary to H7c. However, in support of H7d, for societal argumentation, use by the activist side led to significantly higher argument strength (bindustry = −.607, p < .01) and significantly lower argument suspicion (bindustry = .627, p < .01) than use by the industry side.For the full moderated mediation model, for the industry side, argument suspicion mediated the effects of argumentation strategy on attitudes and voting (conditional indirect effect on attitude: b = −.096, 95% confidence interval [CI]: [−.230, −.006]; conditional indirect effect on voting: b = −.107, 95% CI: [−.261, −.007]), but argument strength did not (conditional indirect effect on attitude: b = −.029, 95% CI: [−.120,.032]; conditional indirect effect on voting: b = −.015, 95% CI: [−.100,.053]), partially supporting H8a. For the activist side, both argument strength (conditional indirect effect on attitude: b = −.312, 95% CI: [−.564, −.101]; conditional indirect effect on voting: b = −.334, 95% CI: [−.578, −.110]) and argument suspicion (conditional indirect effect on attitude: b = −.102, 95% CI: [−.245, −.005]; conditional indirect effect on voting: b = −.081, 95% CI: [−.207, −.001]) mediated the effects of argumentation strategy on attitudes and voting, fully supporting H8b.[12] Experimental Studies 2 and 3To assess the generalizability of our findings across different issues, we conducted two additional experimental studies. Both studies used the same design as Experimental Study 1. Experimental Study 2 (331 U.S.-based participants from the Prolific Academic panel; 44.11% female; Mage = 33.19 years, age range: 18–73 years) focused on expanding a state's bottle deposit law to require deposits for all nonalcoholic, noncarbonated drinks (Expansion of Bottle Deposits Initiative, Massachusetts, November 4, 2014). Experimental Study 3 (340 U.S.-based participants from Prolific Academic; 50.88% female; Mage = 34.70 years, age range: 18–73 years) focused on increasing a state's renewable energy standards, requiring electricity providers to obtain at least 50% of their electricity from renewable sources by 2030 (Renewable Energy Standards Initiative, Arizona, November 6, 2018). The stimuli are presented in Web Appendix W9 and pretests to confirm our argumentation strategy manipulations are included in Web Appendix W11.Results from the two studies were largely identical to those of Experimental Study 1 (see Table 6). For both propositions, as expected and in line with Experimental Study 1, for the industry side, financial argumentation led to higher argument strength and lower argument suspicion than societal argumentation, while for the activist side, societal argumentation led to higher argument strength and lower argument suspicion than financial argumentation. Comparing side on the issue effects for each argumentation strategy, for societal argumentation, use by the activist side led to higher argument strength and lower suspicion than use by the industry side but, as in Experimental Study 1 and contrary to predictions, participants did not perceive financial argumentation as significantly stronger or less suspicious when used by the industry versus the activist side. Across both propositions, argument strength mediated the effect of argumentation strategy on attitudes toward and voting on the issue for both the industry and activist sides. Argument suspicion also had significant mediating effects for the industry and activist sides across both propositions with two exceptions. For the renewable energy standards proposition, argument suspicion mediated the effect of argumentation strategy on voting but not on attitudes toward the industry side, and argument suspicion had a significant mediating effect on attitudes but not on voting for the activist side.[13]GraphTable 6. Response to Argumentation Strategies from Competing Sides: Bottle Deposits and Energy Standards Propositions. 15 Notes: Results from PROCESS Model 58, which includes need for orientation, political affiliation, age, gender (male), education, SES, and argumentation strategy at time 2 (0 −1) as covariates; for bottle deposits study, 10 participants from MA, where proposition was voted on, were dropped from analyses; for the energy standards study, 7 participants from AZ were dropped from analyses; 95% confidence intervals reported for conditional indirect effects; * p < .10, ** p < .05, *** p < .01.In summary, across the three propositions, we obtained consistent support for the mediating role of argument strength and argument suspicion (H8a and H8b). Our results consistently show that societal argumentation is more effective for the activist side: it is more effective than financial argumentation for the activist side (H7b), and is perceived as stronger and less suspicious when used by the activist side than the industry side (H7d). For financial argumentation, results were somewhat mixed: as expected, financial argumentation was more effective than societal argumentation for the industry side (H7a). However, it was not perceived as significantly stronger and less suspicious when used by the industry versus the activist side (H7c). We summarize the results across all experimental studies in Web Appendix W12. Exploration of the Role of Political AffiliationWe contend that battles between industry and activist groups over consumer-related issues tend to be less partisan than other political scenarios such as candidate elections. For example, major early polls reported 77% of Democrats and 70% of Republicans supported Proposition 61, the drug price standards proposition examined in our field study and Experimental Study 1 ([52]; [63]).To gain insight into the impact of partisanship in our context, we performed additional regression analyses to examine the extent to which political affiliation moderates the effects of side on the issue and argumentation strategy on argument strength and argument suspicion across the three issue contexts. Overall, our results indicate that only one of the six interaction effects between political affiliation and argumentation strategy was significant, but all six interaction effects of political affiliation and side on the issue were significant (see Web Appendix W13). The strongest Democrats perceived activist argumentation more favorably than industry argumentation, but there were no significant differences in how Republicans perceived the two sides' argumentation. These results suggest that political affiliation may play a role in how voters view the industry and activist sides. This is consistent with recent polls indicating that the majority of conservatives (liberals) have a positive (negative) view of big business ([55]) and is in line with current polarization along party lines in many domains in the United States ([ 8]; [57]). DiscussionWe find support for our direct-to-public persuasion model across a field study and three experimental studies examining four policy conflict scenarios recently voted on in U.S. state ballot measures. Our findings show that industry and activist arguments play a key role in voting decisions, but industry arguments have less impact than activist arguments. Stronger arguments from both sides lead to more favorable outcomes, but activist groups benefit most. Similarly, industry argument suspicion has limited influence, except for voters who switch their support to the activist side. While societal argumentation is the preferred strategy for the activist side and financial argumentation is preferred for the industry side, the industry's competitive advantage is far less pronounced than expected. Theoretical ImplicationsOur exploration of direct-to-public persuasion increases the breadth of persuasion theory within the marketing domain. Persuasion campaigns in political settings—in which opinions are often strongly held, outcomes are win or lose, and consequences are societal rather than tied to individual consumers—are more complex in their effects than persuasion in commercial settings ([27]; [58]). As there is little theoretical grounding to build on, our research offers new ways of understanding this unique form of persuasion and adds to the scope of sociopolitical legitimacy theory, research on voting behavior, and perceptual fit theory.Our findings related to an asymmetric public response in industry versus activist conflicts caution against assumptions that competing campaigns have an equal opportunity to persuade the public. By addressing unexplored differences in public response to competing campaigns, including legitimacy differences, our research updates persuasion theory by examining how and why arguments from competing sides affect voting behavior. Our results show that competition in politics is uneven, encompassing not just resource imbalances that may advantage one side, but also asymmetries across sides in the extent to which persuasion knowledge plays a role in voters' response (see [17]; [37]). Because the effects of dueling campaign arguments over time are not well known, yet are central to political outcomes, our findings highlight an important direction for future persuasion research.Although theoretical guidance on the impact of argumentation on vote switching behavior is scarce, our results reveal an unexpected role of industry argument suspicion in that, although it has limited effect when voters are considered in aggregate, it is a key driver for the segment of voters who switch their support to the activist side. This finding is novel for persuasion research, as it indicates that the industry argument suspicion effects that drive a change in voter support over the course of a campaign differ somewhat from those driving overall voter support. This implies that response to the competing sides is not only asymmetric but nuanced and that research needs to consider various outcome measures to capture underlying factors.Our finding that the industry side is most effective with a financial argumentation strategy while the activist side is most effective with societal argumentation offers new theoretical support regarding the need for fit between the persuader's identity and arguments. This complements the perceptual fit literature that has traditionally addressed the fit between a company's actions and claims ([64]) or a company's identity and cause-related marketing choices ([54]). We contribute to perceptual fit theory by finding that fit does not have equal importance for both sides, as an image-congruent argumentation strategy is vital for the industry, but voters may be more tolerant of a broader strategy for the activist side. Implications for PracticeOur results offer practical guidance that differs for the industry versus activist sides related to the substance of their arguments and their goals of acquiring or retaining supporters (see Table 7). The findings are relevant for marketers and practitioners who design and implement direct-to-public campaigns with industry associations, corporations, public interest advocacy and activist groups, consultancies, and other policy-focused coalitions ([59]; [65]).GraphTable 7. Key Findings and Guidance for Industries and Activist Groups. While intuitive that both sides benefit from strong arguments, it is most critical for the activist side to be certain that voters will view its arguments as strong, as this is the effect with the highest impact on voting outcomes. This implies that it should be more effective for the industry to focus on diminishing the strength and increasing suspicion of activist arguments. As suspicion of industry arguments has limited impact, the industry has some license to encourage skepticism of activist tactics. This approach may require finesse, however, so as not to trigger a backlash if the issue is salient and the industry is controversial ([57]).Preelection polls often indicate majority support for the activist side, suggesting that the public is initially drawn to a public interest perspective. When the activist side has this early advantage, it needs to retain initial supporters by ensuring that its arguments are strong and by utilizing counterarguments to protect its legitimacy. In contrast, the industry side must focus on acquiring voters and build support with arguments that highlight the hidden complexities and costs of the proposed policy change. As industries often succeed in acquiring supporters during a campaign, as voting nears, the activist side may need to shift its focus in order to cultivate a switching segment.Our results show that the fit between an argumentation strategy and the identity of the persuader is key, especially for the industry side, which is safer emphasizing financial arguments closely aligned to its business sector profile. The activist side has some degrees of freedom as it is viewed positively by the public with either a financial or societal argumentation strategy. While societal argumentation is likely to have the most favorable impact for the activist side, financial argumentation may be necessary to challenge overstated cost analyses used by industry.In summary, our results suggest the industry side does best to follow an aggressive approach of attacking activist-side tactics while using a narrow argumentation strategy. Conversely, the activist side does best by vigilantly preserving its legitimacy in reaction to any industry attacks, while using a broader argumentation strategy. Implications for Public PolicyGiven the consequential outcomes of the ballot measure venue we study and continued increases in funding ($1.24 billion in 2020; https://ballotpedia.org/2020%5fballot%5fmeasures), it is surprising that there is limited emphasis on the transparency of industry and organizational support, such as comprehensive information about donors and the extent of their financial support. Our findings indicate that response to campaign arguments may be driven by legitimacy perceptions of the opposing sides, suggesting that policy makers should facilitate increased information to voters. As the public is primarily informed and persuaded by advertising during political campaigns, including the ballot initiative process, advertising law changes might be the most effective approach to reform ([53]; [70]).The Federal Election Commission regulates campaign finance law and political advertising, but its authority does not extend to ballot measure advertising, which is largely a state issue ([26]). California, under the jurisdiction of the California Fair Political Practices Commission (www.fppc.ca.gov), has among the strictest regulation for ballot measure advertising, following the logic that it is less clear who is responsible for these ads compared with candidate election ads. Because California already requires that top donors be explicitly listed in television, electronic media, and print advertisement as well as in mass mailings and robocalls, it can serve as a model for other states' ballot measure commissions to develop more comprehensive disclosure and increased transparency requirements. Alternatively, the Federal Election Commission could take steps to facilitate more unified federal standards for ballot measure advertisements. Without federal guidelines, consumers will continue to experience a patchwork approach to information that is unequal across states, making it difficult to assess the impact of direct-to-public persuasion on voters' decisions and policy outcomes. Limitations and Directions for Future ResearchOur investigation addresses one type of direct-to-public industry persuasion but not others, such as the more indirect route of shaping legislator decisions by influencing public opinion (for an examination of this issue, see [11]) or expensive direct-to-public industry image campaigns. In our field study, both ballot measures took place in one U.S. state and, thus, lack geographic diversity to mitigate potential unobserved influences. While our experimental studies address well-established argumentation strategies, they represent a partial picture of argumentation used to justify (or sanction) industry practice to the public.There is an important need for more field studies, natural experiments, and other real-world persuasion research initiatives, as well as more research explaining asymmetric public response ([17]). We conducted a posttest to investigate our position that the public perceives that activist groups are more motivated than the industry side to serve the public interest (see Web Appendix W14). While our findings confirm this assertion, future experimental research should examine the extent to which this perception is a driver of voting outcomes. Future studies should explore whether our results indicating that societal argumentation strategies create substantially higher suspicion when used by the industry side could be attenuated for industries with a high proportion of companies with strong corporate social responsibility reputations. Although our findings across four consumer-relevant issues suggest that our hypotheses are likely to generalize across other high-salience issues, further research should investigate whether asymmetry may be muted in contests over lower salience issues.We believe our findings will largely generalize to other contested issue scenarios, including situations where ( 1) the industry is challenging rather than defending the status quo and ( 2) industries battle other industries. When an industry tries to establish or expand a market for growth, it may challenge the policy status quo. A recent example includes the petroleum and gas industry using political clout to expand fracking onto previously protected Native American land in New Mexico ([47]). In these cases, the asymmetric impact of activist arguments having greater impact than industry arguments may be even more pronounced because the industry's self-interested motivation for market expansion may be more obvious to the public. Further, financial argumentation may actually backfire by magnifying the industry-serving objectives unless public attitudes have shifted over time to align with industry initiatives—for example, to promote casino development and marijuana legalization across the United States ([32]; [36]). In situations where industries battle other industries, when there is a legitimacy gap and one industry's public image is more favorable than the other's (e.g., renewable energy industries fighting fossil fuels industries over energy subsidies and standards; see [25]), arguments by the higher legitimacy industry could be more influential in driving outcomes. However, if there is little or no legitimacy gap (e.g., two agricultural industries battling over source of origin labeling laws), asymmetrical effects would not manifest.Despite the fact that direct-to-public persuasion occurs across a variety of industries and scores of issues and has been prevalent for decades, further research is needed because the many dimensions that characterize this form of marketing may represent significant boundary conditions on the impact of argumentation on voting outcomes. Our research offers an important step forward in advancing knowledge of this underexamined area of marketing. " 30,How Physical Stores Enhance Customer Value: The Importance of Product Inspection Depth," The authors investigate the role of the physical store in today's multichannel environment. They posit that one benefit of the store to the retailer is to enhance customer value by providing the physical engagement needed to purchase deep products—products that require ample inspection for customers to make an informed decision. Using a multimethod approach involving a hidden Markov model of transaction data and two experiments, the authors find that buying deep products in the physical store transitions customers to the high-value state more than other product/channel combinations. Findings confirm the hypotheses derived from experiential learning theory. A moderated serial mediation test supports the experiential learning theory–based mechanism for translating physical engagement into customer value: Customers purchase a deep product from the physical store. They reflect on this physical engagement experience, and because it is tangible, concrete, and multisensory, it enables them to develop strong learning about the retailer. This experiential knowledge precipitates repatronage and generalizes to future online purchases in the same category and in adjacent categories, thus contributing to higher customer value. This research suggests that multichannel retailers use a combination of right-channel and right-product strategies for customer development and provides implications for experiential retail designs.","The online channel has assumed a dominant role in many industries, the result of a 15% annual growth in e-commerce (U.S. [52]). In this environment, retailers seem ambivalent about the role of physical stores. Industry surveys show that many consumers still prefer them: ""Half of the shoppers surveyed stated that they preferred to shop with online retailers who also operated physical stores"" ([47], p. 7; see also [ 7]; [43]). However, many retailers such as Macy's, Walgreens, and Bath & Body Works are closing physical stores ([41]). At the same time, digital-native online retailers such as Amazon, Alibaba, Blue Nile, Warby Parker, Bonobos, Google, and Indochino are opening them ([57]). Given these mixed messages, we find it natural to ask, ""What in fact is the value of the physical store?""To answer this question, we first recognize that consumers buy products, not channels. They must decide which products to buy in which channels. [31] advocate for studying this joint product/channel decision. They maintain that the product and channel choice processes are ""intertwined"" (p. 320) and that ""there is significant academic and managerial motivation for the studying the interrelationships between brand and channel choice"" (p. 320). Although, strictly speaking, they discuss channel and brand choice, their message can also be interpreted as calling on academics to research ""the consumer's decision of where and what to buy"" (p. 329).Our research draws on this call to action and the seismic shifts in the retail landscape to propose and empirically examine a central hypothesis: physical stores can enhance customer profitability by providing the physical engagement that customers value when purchasing ""deep"" products. We argue that products differ in the amount of inspection customers need to make a purchase. Some products require relatively ""shallow"" inspection, where a picture and written description suffice (e.g., a mobile phone charger), whereas other products require ""deep"" inspection, such as touch and physical interaction (e.g., a shirt). Drawing from experiential learning theory (ELT), our thesis is that customers value physical engagement when buying deep products and that the store provides such physical engagement, creating a favorable learning experience that increases repatronage. The lesson is that the product/channel purchase combination—deep product purchases in-store—develops more profitable customers.Our research objective is therefore to investigate the following questions: ( 1) Does buying deep products in the physical store[ 5] enhance customer value more than other product/channel combinations? ( 2) If so, what are the implications for the customer's future channel choices?We adopt a multimethod approach to answer these questions. In Study 1, we analyze customer-level transaction data for 50,387 customers of a large multichannel retail chain that sells outdoor recreation gear, sporting goods, and clothing. We first classify products as ""deep"" and ""shallow"" on the basis of the novel concept of product inspection depth that builds on [23] concept of digital and nondigital attributes and [37] findings around the importance of haptic information to consumer experiences. We then use a hidden Markov model (HMM) to examine consumers' product/channel choice dynamics and uncover two latent states: ( 1) a low-value state characterized by lower purchase frequency and profitability and ( 2) a high-value state characterized by higher purchase frequency and higher profitability. We find that customers are more likely to transition to the high-value state and remain there to the extent they have purchased deep products in the physical store.To replicate these findings and understand the underlying process, we conduct two lab experiments. Study 2 verifies that the deep product/physical store combination produces the highest repatronage intentions. Moderated serial mediation analysis supports the following process proposed by ELT: concrete experience → reflection on physical engagement → hypothesized learning → repatronage. This finding suggests that purchasing deep products in-store provides consumers with concrete, tangible, multisensory experiences that enable them to reflect on and then generate hypothesized learning that encourage them to repatronize the retailer, thus enhancing customer value.Experiential learning posits that on gaining experience, consumers generalize their learning beyond the contexts they have experienced. Study 3 verifies that once customers have purchased a deep product in a physical store, they are more amenable to purchasing the same as well as adjacent deep products online from the retailer in the future. They thus generalize from the retailer's store to its website and to adjacent deep product categories.Our findings support the trend of online retailers establishing an offline presence to enhance customer value by providing customers with concrete, tangible, multisensory experiences. This corroborates that many consumers still prefer to buy in-store. A recent survey found that ""if given the opportunity, 71% of consumers said they would even prefer to shop at an Amazon store over Amazon.com"" ([49]). Another survey revealed that ""50% of shoe buyers, 64% of sports equipment buyers, 59% of furniture buyers and 68% of jewelry buyers still prefer the physical store"" ([43]). Thus, despite two decades of innovation aimed at making e-commerce more engaging, many consumers still perceive the benefits of buying deep products in physical stores.Practitioner quotes support consumers' needs for concrete, tangible, multisensory experiences, which facilitate physical engagement, for why online retailers seek physical store presence:[Alibaba's physical stores are] providing an option for consumers to physically inspect, touch and feel products before purchase. This appears to be the right strategy. The physical store should attract … online shoppers, who want a more human shopping experience (Trefis [51]).Many stores will be giving customers an experience and providing insight and information. Want new pants? Find your size and the style you like in the store. Looking for a new smartphone or tablet? Try them out at a store and have a clerk walk you through the different features ([ 9]).With certain products, seeing and feeling makes a difference … even the most elegant descriptions and images can't replace the feel of organic, high thread count cotton sheets ([57]).These quotes echo our proposition that the physical store provides customers with the engagement they value when purchasing deep products. The fact that online retailers are pursuing an offline presence today validates our findings and supports our physical engagement theme. In turn, our findings support this trend. Our findings also suggest that online retailers who cannot afford the investment required for a physical presence should mimic the physical engagement found in stores and make the digital experience more concrete, tangible, and multisensory.In summary, we offer three key findings. First, buying deep products in the physical store increases long-term customer value. Second, consumers who purchase deep products in-store are subsequently more likely to buy the same and adjacent deep product categories online. Third, an important underlying mechanism is experiential learning. We also find that direct mail marketing encourages customers to purchase deep products in-store and increases customer value, suggesting that retailers can onboard new customers and revive lapsed customers via the promotion of deep products and in-store purchasing.These findings make the following contributions. First, we empirically identify an important role of physical stores, which is to enhance customer value by providing the physical engagement needed to purchase deep products. Second, we provide evidence that physical stores fulfill this role through experiential learning. Third, we advance theory by introducing the notion of ""product inspection depth"" and highlighting the distinction between physical engagement and digital engagement. Finally, we expand multichannel research that has focused on channel choice ([50]; [53]; [58]) by demonstrating that management of the joint product/channel decision is crucial for better understanding customer behavior. Framework and PredictionsWe aim to identify which product/channel purchase combination enhances customer value more than other combinations. We posit that customers value physical engagement when buying deep products. The store provides this engagement, creating a favorable learning experience that increases patronage and customer profitability. Product Inspection Depth and Physical EngagementWe distinguish products in terms of inspection depth, defined as the degree to which customers examine the product to make an informed purchase decision. Inspection depth can be ordered along a continuum: ( 1) pictures and descriptions are adequate, ( 2) visual inspection of the product is needed, ( 3) touching the product is needed, and ( 4) interaction with the product is needed (e.g., trying on, testing). We refer to products that require less inspection as ""shallow products""; those that require more inspection we refer to as ""deep products.""The concept of product inspection depth follows from the literature that examines how extensively the customer must examine a product to make an informed purchase. [45] stress the importance of physical inspection in the fashion industry. [14] find the inability to inspect shoes, DVD players, flowers, and food is an impediment to patronizing online stores, but not so for books and toothpaste. [33] suggest that profits for the multichannel firm decrease when consumers find it important to inspect the product before purchase. Product inspection depth is rooted in [30] theory of search and experience goods. Search goods can be evaluated prior to purchase, whereas experience goods need to be consumed to be evaluated. Relatedly, [23], pp. 487–88) advance the concept of digital attributes, which can be ""communicated online"" versus nondigital attributes, which ""can only be evaluated through physical inspection.""Product inspection depth synthesizes these ideas yet differs in important ways. For example, it differs from experience/search in that a deep product does not have to be consumed to be evaluated—it just needs to be inspected. It extends digital attributes, such as appearance, into the physical domain (e.g., ""I will try on this clothing to see how it actually looks on me""). Furthermore, it is not confounded with price. For instance, a pair of shoes requires deeper inspection than a (higher-priced) computer, whose specifications can be read off a product description. Inspection depth is particularly relevant to multichannel shopping, where the ability to inspect differs by channel.To acquire product inspection depth, consumers need to physically examine, inspect, or even interact with the product. That is, the customer has to physically engage with the product. More generally, the literature defines engagement as ""a behavioral manifestation toward the brand, beyond purchase"" ([54], p. 253). This manifestation can include touching and examining the product, reading descriptions, watching a demonstration, reading reviews, and interacting with a sales representative.The literature further delineates two forms of engagement: digital and physical ([55]). Digital engagement entails nonphysical actions such as seeing the product on a printed image. Formally, we define ""physical engagement"" as when the customer goes beyond visual inspection to gain multisensory knowledge of the product (e.g., by touching and using it). [38] link physical touch to object valuation, and [39] link touch to persuasion, suggesting that physical engagement can increase customer satisfaction. The potential for physical engagement differs by channel. The physical versus digital distinction is important because physical stores offer both physical and nonphysical engagement, whereas the online channel only offers nonphysical (digital) engagement. Experiential Learning TheoryThe product inspection depth consumers acquire through physical engagement defines a shopping experience. What do consumers learn from this experience? This is the bailiwick of experiential learning theory (ELT). David Kolb developed ELT as a synthesis of work by Lewin, Piaget, and others ([15], [16]; [17]; [28]). It is relevant for our purposes because it translates experience into learning. In Kolb's words, ""Learning is the process whereby knowledge is created through the transformation of experience"" ([16], p. 38), and ""Knowledge results from the combination of grasping and transforming experience"" ([16], p. 41).ELT is a process whereby people learn in four recursive stages: experiencing, reflecting, thinking, and acting ([17], p. 194). People first experience something concrete or tangible. They then reflect on the experience. Reflection enables people to hypothesize what they have learned from the experience and the extent to which this learning generalizes beyond the recent experience. People then act (i.e., test this hypothesis when the opportunity arises). The action provides more experience, and the four-stage process repeats.[ 6]A review of the ELT literature reveals four themes we draw on in forming our hypotheses. First, experiential learning builds on concrete and tangible experiences ([16]). [16], p. 21) notes, ""The emphasis is on here-and-now concrete experience,"" and ""Immediate personal experience is the focal point for learning, giving life, texture, and subjective personal meaning to abstract concepts.""Second, experiential learning is a feedback process: Consumers develop hypothesized learning from experience. They use subsequent experiences to test how well these hypotheses generalize, modifying them as needed. ""Learning is described as a process whereby concepts are derived from and continuously modified by experience"" ([16], p. 26).Third, experiential learning draws on the five senses. Under the rubric ""sensory marketing,"" researchers have connected experience to the five senses—touch, taste, smell, hearing, and seeing ([18]). Thus, three findings from sensory marketing prove critical to our hypothesis development. First, touch experience influences attitudes. Research has found that touch leads to more confident conclusions ([37]), is more persuasive ([39]), generates affect ([38]), and influences quality judgments ([ 1]). Second, multisensory experiences are more effective in generating learning than are single sensory experiences ([19]). Finally, touch can be an end in itself or a gateway to enhancing other senses, particularly visualization ([35]). Consider the case of buying jewelry and wristwatches. Touching the jewelry or the watch to feel its texture and weight distribution is valuable in itself, but it also enhances visualization, as one can pick it up, view it in natural light from various angles, and try it on to see how it looks and feels. Touching and seeing naturally go together.Figure 1 integrates product inspection depth, engagement, experiential learning, and customer value. The process starts with a shopping experience, characterized by the type of product the consumer buys (deep vs. shallow) and the purchase channel. This initiates the experiential learning process. Iterating through ELT yields learning that consumers want to confirm and determine if it generalizes to other contexts. These tests take the form of future shopping behaviors, which serve as the experiences that initiate future ELT cycles.Graph: Figure 1. Experiential learning: translating engagement to future behavior. Predictions The impact of deep/offline purchase on customer valueDeep products require inspection, touch, and trial. The store provides this physical engagement, and the shopping experience therefore is concrete, tangible, and multisensory—prerequisites for effective experiential learning. These factors in synchrony encourage deeper reflection, stronger hypothesized learning and potential generalizations, and, ultimately, more action.Importantly, we assume that providing physical engagement for a deep product purchase enables the consumer to make a more informed decision. The experience, therefore, will be positive, encouraging favorable learning that propel the consumer toward repatronage and higher customer value. We therefore hypothesize, H1: Purchasing deep products in the physical store is associated with higher future customer value more than any other product/channel purchase combination. The role of experiential learning on the impact of deep/offline purchase on customer valueELT contributes to the translation of deep products/in-store to future customer value. It suggests the following mechanism underlying H1: customers purchase a deep product from the physical store. They reflect on this physical engagement experience, and because it is tangible, concrete, and multisensory, it enables them to develop strong hypotheses of what they learned about the retailer. Because they are satisfied with their purchase, this learning is favorable, precipitating repatronage.In summary, the mechanism is as follows: customers purchase a deep product in-store → they reflect on the physical engagement experience → they hypothesize favorable learning → they repatronize the retailer. This parallels ELT's process of experience → reflection → hypothesized learning → action. We thus predict: H2: Experiential learning contributes to the process by which purchasing deep products in the physical store is associated with higher future customer value.Two assumptions underlie H1 and H2. First, the customer purchases a satisfying product. It is possible that despite the concrete experience, the customer emerges dissatisfied. Experiential learning is still at play, but the consumer learns that this retailer is not suitable, and customer value declines. Second, the customer's favorable experience spills over to both the retailer and the brand. Thus, H1 and H2 assume the customer emerges from the deep/offline experience with favorable learning that transfers to the retailer.Importantly, H1 is comparative; it maintains that deep/offline purchasing enhances customer value more than any other product/channel combination for the following reasons. First, the online channel entails only one sense—visual. Thus, the online experience is less concrete, less tangible, and not multisensory, and the consumer learns less. Second, shallow/offline purchasing has the potential to provide a concrete, tangible, and multisensory experience because the store offers this opportunity. However, by definition, shallow products do not require this physical engagement. As a result, the customer does not reflect enough to generate strong hypothesized learning, and repatronage is not enhanced as much. The impact of a deep/offline purchase on future deep/online purchasesELT posits that customers will test the extent to which their learning generalize beyond their experience to date. We consider two types of generalization. First, customers may generalize to a new channel. Consider customers buying a shirt in the physical store, and assume that they hypothesize from this experience that the retailer is a good place to buy shirts. They can then see how well this learning generalizes to another channel by purchasing deep products online from the retailer's website. This lets them test for generalization while taking advantage of the convenience of the website. As elaborated in our discussion of H1 and H2, customers who purchase shallow products in the physical store do not learn enough to encourage them to explore whether to purchase deep products online. In other words, these consumers are less apt to experiment with the website if they need a shirt. We thus hypothesize: H3: Purchasing deep products in the physical store is associated with buying deep products online in the future, compared with purchasing shallow products in the physical store.Second, learning inferred by deep/offline customers can generalize not only to new channels but also to new products. Product generalization can occur because the initial purchase experience allows customers to learn about the retailer's overall product quality and product fit—factors that are especially important for deep products. Obviously, it is easier to generalize to something that is most related to the current context, and we believe the strongest impact of buying a shirt offline will be buying a shirt online in the future. But the generalization could extend to adjacent deep products such as sweaters, coats, and other apparel, and possibly to different product categories altogether. We thus hypothesize: H4: Purchasing a particular deep product in the physical store is associated with buying adjacent deep product categories online in the future. Overview of StudiesStudy 1 uses retail transactional data and an HMM to verify H1 and H3. We test whether the translation of product/channel into customer value is most favorable for the deep/store combination (H1) and whether this combination encourages future usage of the online channel (H3). We conduct two randomized experiments in Studies 2 and 3 to replicate Study 1's findings. In addition, Study 2 tests the hypothesized learning mechanism (H2), and Study 3 tests product generalization (H4). Study 1: An HMM of Customer Product/Channel Dynamics DataOur data are from a national retailer that sells outdoor recreation gear, sporting goods, and clothing in 140 retail stores and on its website. The data chronicle customer-level purchase occasions from January 2003 to July 2005. For each purchase occasion, we observe stockkeeping units (SKUs) purchased, price, purchase amount (dollars spent on the entire order), cost of goods sold, channel choice, timing, and returns. We know each customer's zip code and tenure with the retailer.Our sample contains 50,387 customers buying more than 30,000 SKUs on 585,577 purchase occasions, an average of 12 purchase occasions per customer. Table 1 shows that online purchases make up 11.1% of purchase occasions. Because we are interested in dynamics, we select only customers who had at least two purchase occasions. Of these, 8,391 are ""new customers,"" with 80,751 purchase occasions, acquired after the beginning of the data set. Deep products constitute 54.7% of purchases, with an average spend of $56.21 per purchase occasion. The firm uses direct mail to communicate new styles and special events. On average, customers receive 19.6 direct mail pieces annually. The retailer does not target on the basis of past purchases. Prices and cost of goods sold do not vary between online and offline. Only 5.6% of customers purchased the same SKU more than once, either on the same purchase occasion or over multiple purchase occasions. So there are few instances of rebuying the same product. Half of the customers shop in a single channel; the others shop both in-store and online. Consistent with previous research, multichannel shoppers are more profitable (see Web Appendix Table W1.1).GraphTable 1. Descriptive Statistics per Customer. The retailer categorizes the 30,000 SKUs into ten ""specialties,"" such as camping, travel, cycling, snow sports, and clothing, followed by 373 ""classes"" or categories, such as jackets, pants, and shorts, and finally specific SKUs. For details, see Web Appendix Table W2.1.Three independent judges rated each of the 373 product categories on inspection depth as well as digital or nondigital (Web Appendix W3) on a scale of 1 to 7. Intercoder reliability for inspection depth is.92. This suggests that the inspection depth concept is robust across coders and covers dimensions such as touch and interaction. The correlation between inspection and digital/nondigital ratings is.77, thus offering discriminant validity from digital/nondigital.[ 7] Not surprisingly, clothing and footwear generally have high ratings, but there is much variation within a specialty (Web Appendix Figure W3.1). This suggests that specialties are not perfect indicators of inspection depth. For example, within footwear, hiking boots are rated 7, women's sandals are rated 4, and insoles are rated 2 (on a 7-point scale). Deep products are not necessarily more expensive than shallow products; the correlation between price and inspection depth is.12 and insignificant. We categorize products as deep or shallow using a median split. This enables us to model purchase amounts of each type on each purchase occasion.[ 8] Model-Free EvidenceOur key hypothesis is that buying deep products in the physical store increases future customer value. New customers provide model-free evidence for this. Table 2 shows four cohorts of new customers defined by when they are acquired. Profits for the one-year period after acquisition differ depending on the first product/channel choices; buying deep products offline as the first purchase yields the highest profit, consistent with H1.GraphTable 2. Model-Free Evidence: First Product/Channel Choice and One-Year Profit. Figure 2 illustrates dynamics. It depicts product and channel choices for new customers' first purchase and the same set of new customers on their eighth purchase. We see that 5.76% of first purchases are deep products bought online. This increases to 8.45% by the eighth purchase. This finding is consistent with H3 and shows that new customers' buying patterns evolve and alleviates the concern that new customers' purchase patterns are set before the retailer acquires them.Graph: Figure 2. Model-free evidence of product/channel choice evolution.It is still possible that new customers self-select into the relationship with the retailer. We address this in Study 1 by ( 1) developing a model designed to flexibly detect dynamics and control for unobserved customer heterogeneity, ( 2) separately analyzing new and existing customers via robustness checks, and ( 3) conducting a propensity scoring analysis. Studies 2 and 3 alleviate the self-selection concern using random treatment assignment in experiments. Modeling FrameworkWe use a multivariate HMM to study customers' joint decisions for channel choice, purchase amount, and interpurchase time. HMMs are often employed to study the dynamics of customer–firm relationships (e.g., [21]; [24]; [26]; [29]; [32]; [46]; [61]; [62]). HMMs incorporate experiential learning in that they include dynamics and feedback and model changes in behavior arising from experience (reflected in ""latent states""). HMMs also identify the drivers of these dynamics by studying customer transitions between latent states. A simpler model such as regression would have difficulty capturing these dynamics and rich insights. Furthermore, HMMs can distinguish temporal dynamics from customer heterogeneity. We will model time-invariant customer heterogeneity as well as dynamics.We use the HMM to discern when the customer is in a high- versus low-value state and predict transitions between these latent states by descriptors such as customers' previous purchases of deep versus shallow products and their use of offline versus online channels. In this way, the HMM provides tests for H1 and H3.Specifically, we model a customer's purchase occasion by four interrelated decisions: ( 1) channel choice, ( 2) purchase amount (in dollars) of deep products, ( 3) purchase amount (in dollars) of shallow products, and ( 4) purchase timing (in terms of interpurchase time). These four dependent variables not only paint a rich and multifaceted picture of customer behaviors beyond overall purchase amount but also enable us to calculate customer value within a particular time frame. We incorporate covariates in customers' utility functions and thus predict customers' decisions at each purchase occasion. We include covariates in transition functions to predict how customers migrate between latent states.Because HMMs are popular in marketing, we detail the specific components of our HMM to Web Appendix W4. Next, we highlight the covariates in the specification. Covariates in the HMM Previous channel choiceH1 and H3 both predict that previous channel choices influence future customer value. We include the customer's cumulative number of channel choices prior to purchase occasion j, offline_choices(j − 1) and online_choices(j − 1),[ 9] for the store (offline) and website (online), respectively. This is consistent with [22], [48], and [59].[10] MarketingDirect mail is the only firm-initiated marketing activity in the data and did not promote specific channels. Discussion with management revealed that communications were not customer-targeted, and we test and confirm that there is no endogenous relationship between customers' past purchase behavior and the likelihood of receiving direct mail. Thus, it reflects a baseline advertising effect. The variable marketingj equals the number of mailings the customer received within 30 days prior to purchase occasion j. We use a squared term, marketingj2 , to capture decreasing returns.[11] HolidayThe variable holidayj indicates whether purchase occasion j occurs within two days preceding the following gift-giving holidays: Mother's Day, Father's Day, Valentine's Day, Christmas Eve, and New Year's Day. Because last-minute online shopping risks that a gift will not arrive on time, people may be more likely to shop offline when it is very close to these holidays. Purchase amountWe measure customer spend (in dollars) on deep and shallow products on previous purchase occasion j − 1 (deep_amount[j − 1] and shallow_amount[j − 1]). Both H1 and H3 predict that the type of product purchased is crucial for determining future customer value. Customer tenureTenure (tenurej) denotes how long the customer has been purchasing from the retailer as of purchase occasion j. It represents the length of the relationship. Product returnsWe calculate cumulative returns prior to the current purchase occasion (in dollars) and distinguish between deep and shallow product returns (deep_returns[j − 1] and shallow_returns[j − 1]). [40] find that product returns enhance customer relationships. Interpurchase timeWe define interpurchase_timej as the length of time between the previous and current purchase occasion. Shorter interpurchase time indicates more frequent purchases and, thus, a more valuable customer. Longer times could indicate lapses in loyalty or changes in lifestyle, which could influence subsequent channel or product choice. Specifying Utility and Transition FunctionsHMMs model latent states and estimate ""transition functions"" that predict how the customer migrates in and out of these states over time. HMMs characterize each state by its own set of utility functions—in our case, one for each of the four customer decisions we model. Following HMM conventions, we include covariates expected to have an immediate impact in the utility functions and covariates expected to have a long-term impact in the transition functions.Accordingly, we include previous channel/product decisions—offline_choices(j − 1), online_ choices(j − 1), deep_amount(j − 1), and shallow_amount(j − 1)—in all four utility functions to reflect ELT's dictum that customers test what they learn from experience by taking action—all four behaviors are actions. We include marketingj and marketingj2 in the utility functions because direct mail can have an immediate reminder impact. We expect channel choice to be driven by proximity to a holiday. Thus, holidayj enters in the utility function for channel choice.We also include previous channel/product decisions (offline_choices[j − 1], online_choices[j − 1], deep_amount[j – 1], shallow_amount[j − 1]) in the transition functions to test our hypotheses that these influence future customer value. Direct mail is advertising that can have long-term effects, so we include marketingj and marketingj2 in the transition equations. Drawing on [40], we include returns (deep_returns(j − 1] and shallow_returns[j − 1]) in the transition functions. As noted previously, long customer tenure and short interpurchase times could proxy for a strong long-term relationship, so we include tenurei and interpurchase_timej in the transition functions.Note that we use lagged variables in the utility and transition functions to capture dynamics (how previous decisions drive current decisions and state transitions). We are particularly interested in how previous channel and product choices determine future customer value.We do not include current prices as a covariate. From a theoretical standpoint, including current prices in the purchase timing and channel choice models would make the difficult-to-support assumption that customers make these decisions based on prices they do not observe until after they make those decisions. As a robustness check, we included monthly fixed effects and a monthly price index of top 30 best-selling items as covariates in the utility equations. Results, most importantly those pertaining to H1 and H3, were substantively the same.[12] Heterogeneity and EstimationCapturing customer heterogeneity is crucial for distinguishing temporal dynamics from time-invariant customer heterogeneity ([13]). We do this by adding latent class segmentation to the HMM. This allows the coefficients for the transition functions and the four utility functions to vary across segments.We use Markov chain Monte Carlo (MCMC) methods for estimation and use the adaptive Metropolis procedure ([ 3]) to improve mixing and convergence. We use the first 24 months of data for training and the last seven months for testing. We obtain our estimates from the last 50,000 draws from an overall MCMC run of 200,000 iterations. We assessed convergence by monitoring the time-series of the MCMC draws. Interpreting the Results Choosing the number of states and latent classes (segments)To determine the number of HMM states and the number of segments, we consider the in-sample log-marginal density, deviance information criterion, and predictive log-likelihood on the validation sample. Drawing on these criteria, we find that a two-state, two-segment HMM exhibits the best performance. It captures dynamics and heterogeneity while keeping model complexity in check. Therefore, we adopt this model. Web Appendix Table W4.1 shows these criteria for various permutations of the HMM. The table shows that the two-state, two-segment HMM is better than models that do not include dynamics, do not include heterogeneity, or include heterogeneity but as a continuum rather than discrete segments.[13] Interpreting the statesWe used methods described in [27] to infer each customer's state membership at each purchase occasion. We assign customers to the state with the highest probability to which they belong (in our two-state case, greater than.5). Table 3 shows average customer characteristics for each state. The interpretation is clear: customers in state 1 have longer interpurchase times (i.e., buy less frequently) and generate less revenue and profit. We label state 1 ""low-value"" and state 2 ""high-value.""GraphTable 3. Description of the Two HMM States. Interpreting the segmentsWe assign customers to the latent segment to which they have the highest probability of belonging. Table 4, Panel A, shows that segment 1 is more profitable than segment 2, has a more balanced mix between in-store and online buying, transitions more quickly to the high-value state, and lives closer to the retail store. The channel mix and higher profitability suggest that segment 1 is the ""multichannel segment."" Table 4, Panel B, shows that customers in this segment buy more deep products, especially when they move to the high-value state. Table 4, Panel C, shows that customers in this segment are more likely to migrate to the high-value state and remain once they get there. Segment 2 focuses on offline—92% of these customers' purchase occasions are in-store (95% when they are in the low-value state and 76% when they are in the high-value state). Table 4, Panel C, further shows that segment 2 customers are less likely to transition to the high-value state. Accordingly, we label segment 1 ""multichannel"" and segment 2 ""offline.""GraphTable 4. Segment Descriptions and Migration Probabilities. Interpreting the transition functionsTable 5 shows the parameter estimates for the transition functions. A positive coefficient for the low-value state means that customers in that state are more likely to move from low value to high value as the covariate increases; a positive coefficient for customers in the high-value state means they are more likely to stay high value. In both segments, previous choices of the offline channels drive customers to high value or keep them there. In contrast, online choices decrease the likelihood that low-value customers move to the high-value state, as well as the chance they remain high value. Marketing drives customers to the high-value state and keeps them there. Purchasing deep products drives customers to the high-value state and keeps them there, whereas purchasing shallow products drives customers to the low-value state and keeps them there. Longer tenure transitions customers to high value, whereas longer interpurchase times move customers to low value. Importantly, the transition equations reveal plenty of dynamics that vary by segment.GraphTable 5. HMM Transition Functions Parameter Estimates. 1 Notes: Positive coefficient means variable increases transition among customers in the low-value state and increases the probability of staying in high-value if the customer is already there. Interpreting the customer decision modelsTable 6 contains parameter estimates for the four decision models (i.e., the utility functions) for both segments and both states. We also calculated marginal effects (Web Appendix W6), which are consistent with the estimates in Table 6.GraphTable 6. Parameter Estimates for the Four Decision Utility Functions. 2 aPositive coefficient means variable increases likelihood of choosing offline channel.3 bPositive coefficient means variable decreases interpurchase time.Table 6, Panel A, displays the utility functions for the channel choice decision. The offline and online previous choice coefficients are mostly positive, suggesting that previous purchase in either channel encourages customers to buy in-store for their next purchase. Interestingly, higher previous deep product purchases mostly encourage customers to buy online, whereas shallow product purchases encourage them to buy offline. Marketing primarily stimulates offline purchases, with the exception of multichannel/high-value purchases. Holiday shopping more likely takes place in the physical store, as expected. Overall, we find strong effects of previous channel choice, previous spending by product type, and marketing.Table 6, Panels B and C, show the utility functions for deep and shallow purchase amount. In general, previous offline purchase increases both deep and shallow spend, whereas previous online purchase has the opposite impact. Marketing increases deep and shallow spend, with a stronger impact on deep products. Previous deep product spend begets higher spend for both deep and shallow products. Shallow has the same direction of impact, albeit relatively smaller. Table 6, Panel D, shows that, for all segments and states, previous offline purchase decreases interpurchase time, possibly because the customer is more satisfied and thus buys again sooner. Testing HypothesesOur model relies on temporal precedence, the association between previous and subsequent decisions, to support a causal interpretation of the results. As we show next, these estimated dynamics suggest that buying deep products in the physical store creates higher customer value and encourages customers to buy online in the future. However, as with any model of field data, we cannot claim the model unequivocally establishes causation. This is one reason we rely not only on Study 1 but also on controlled experiments that use randomization.H1 hypothesizes that purchasing deep products offline is more likely to transition the customer to the high-value state than any other product/channel combination. The transition function estimates in Table 5 support this. Coefficients for deep_amount(j − 1) (row 7) and offline_choices(j − 1) (row 3) are positive in all four transition functions. Coefficients for online_ choices(j − 1) (row 4) are negative, so online purchases make it less likely that the customer will transition to high value. The coefficients for shallow_amount(j − 1) (row 8) are negative—or, at best in the offline segment, positive but far smaller in magnitude than the coefficients for deep_amount(j − 1). Overall, we find that buying deep products offline is most positively associated with transitioning to the high-value state (i.e., to higher customer value). Study 1 thus supports H1.H3 proposes that customers who buy deep products offline are more likely to purchase deep products online in the future. We have shown that deep/offline purchasing drives customers to the high-value state. Table 3 then shows that these high-value customers purchase a higher percentage of their deep products online (vs. in-store) than do the low-value customers (29% vs. 11%, row 4). Table 4, Panel B, further illustrates this at the segment level: high-value multichannel customers make 43% of their deep product purchases online, compared with 15% if they are low value. The same holds true for the offline segment: these customers purchase 26% of their deep products online if they are in the high-value state, compared with 11% if they are in the low-value state. Study 1 thus also supports H3. Robustness Checks New versus existing customersWe ran the model only on existing customers (41,996 customers, 504,826 purchase occasions). The substantive results were very similar to those for the full data set, indicating that the evolution of customer behavior based on channel/product experiences exists for both new and existing customers. New customers' first channel/product choiceWe tested whether the results in Table 2 are due to self-selection—that is, that new customers who start by purchasing deep products in-store already prefer the retailer. We conducted propensity score matching with new customers whose first purchase is deep products in-store as the treatment group and all other new customers as potential controls. We matched on ( 1) demographic variables extracted from each customer's zip code and ( 2) variables calculated using data from the first two months after the initial purchase. These included distance to store, city versus rural, average prices paid, the gender category of the products purchased, purchase of children's products, number of categories purchased per visit, and average interpurchase time. We then calculated subsequent profit, excluding those first two months.The average treatment effect (incremental value among new customers with deep/in-store as the first purchase) is +$132.74, suggesting that purchasing deep products in-store generates higher long-term customer value (Web Appendix W7). The Rosenbaum test ([44]) states that our treatment effect is significant even if the impact of an unobserved covariate were to increase the odds ratio of buying deep products offline by 50% (Γ = 1.5; see Web Appendix Tables W7.2 and W7.3). Product definitionWe ran the model with products classified as digital/nondigital instead of deep/shallow. The same substantive results hold for the digital/nondigital classification, with slightly worse model fit and prediction compared with the deep/shallow classification (deviance information criterion = 796,253 for digital/nondigital vs. 792,594 for deep/shallow, predictive likelihood = −198,237 for digital/nondigital vs. −194,899 for deep/shallow). This suggests that the deep/shallow product categorization yields a better-fitting model but similar findings to a ""digital/nondigital"" categorization. It also suggests that the sensory-rich inspection depth concept is particularly relevant in multichannel environments. Use of median split for categorizing productsOur analysis categorizes deep and shallow products using a median split. We examined how much our results would change if we used different split thresholds. We reran the model using thresholds ranging from 30% to 70% (i.e., from a 30th percentile rating used to classify a product as deep up to a 70th percentile threshold). Web Appendix W8 indicates that the 50/50 (and 60/40) splits provide the best fit and performance.[14] In addition, our substantive results hold up between 30% or 70% thresholds, suggesting that the results are robust within a reasonable range of rules for classifying products as deep versus shallow. Finally, the predictive likelihood for the 30% threshold is better than that of 70% threshold, suggesting that it is safer to classify shallow as deep than deep as shallow. Marketing Simulation for TargetingThe positive coefficient for marketingj in Table 5 (row 5) suggests that direct mail marketing moves customers to the higher-value state or keeps them there if they are already in a high-value state. A reasonable strategy is to target marketing to increase the probability that customers are in the high-value state. The question is, which customers should be targeted on the basis of their current state, segment membership, and previous product/channel choice?We conducted a simulation to investigate this question. Details are in Web Appendix W10. We used model parameters to simulate purchase behavior over a 30-month horizon. In the base case, marketing is set so that each customer receives two direct mail pieces per month. In the ""+1"" case, we increased this to three per month. One could use dynamic programming to optimize targeting, but our purpose is simply to demonstrate the potential of targeting.Profits in the base case were $127.36 per customer ($131.21 under the +1 strategy).[15] We found that the +1 strategy increased the probability of transitioning to or staying in the high-value state, ""Prob(Hi),"" for all customers except multichannel customers who currently are in a high-value state. They already have a high probability of staying high value (86.78%), so there is not much to gain by increasing marketing. A key result is that the gain in Prob(Hi) is largest for customers who just bought shallow products. For example, we found the gain for offline-segment low-value customers who just bought shallow offline is 29.20% − 24.67% = 4.53%, while the gain for those who just bought deep offline is 36.39% − 33.27% = 3.12%. This finding is consistent with H1: deep offline purchases naturally boost customers to a high-value state, so they have less need for marketing. Study 2: Replicating H 1 and Testing the Experiential Learning Mechanism (H 2)H1, supported by the HMM, proposes that one ""sweet spot"" for generating future customer value is for the customer to purchase deep products in the physical store. Study 2 employs a lab experiment to replicate H1 and test H2, the ELT mechanism we propose underlies it: deep product purchased in-store → physical engagement → favorable learning → repatronize the retailer. MethodOur sample is 411 Amazon Mechanical Turk subjects. The average online and store patronage experience, age, and gender are statistically equal across treatment groups (details in Web Appendix Table W11.1).We use a 2 (deep vs. shallow product) × 2 (physical store vs. online) between-subjects design with random assignment to treatment. We used ""sports shirt"" for the deep product and ""portable cell phone charger"" for the shallow product.The survey (Web Appendix W11) instructed subjects that there is ""a new sports and outdoor-gear retailer in town"" and that ""you have never shopped at this retailer."" We then asked, ""Now, imagine that you shop at this retailer for the first time. You visit its physical store (website) and buy a sports shirt (portable cell phone charger) that costs $40. Please take a few minutes to describe in detail the specific steps you would have taken to purchase the sports shirt (portable cell phone charger) in this new physical store (on this new website)."" We provided a text box for subjects to write a description of the steps they would have undertaken.We then asked subjects to state their intention to shop at this retailer again, using the [ 4] three-item repatronage scale (e.g., ""I would be willing to buy from this retailer again in the future"" [1 = ""Strongly disagree,"" and 7 = ""Strongly agree""]). We asked subjects to rate how much they thought they would have learned about the retailer's ""product offerings and quality, as a result of this experience,"" on a seven-point scale. This was followed by a question asking subjects to rate the product they bought on product inspection depth (1 = ""Picture and description would be adequate,"" 2 = ""Visual inspection of actual product needed,"" 3 = ""Touch of product needed,"" and 4 = ""Interaction of the product needed [e.g., trying on, testing the features]""). This last step enables us to verify the deep versus shallow product manipulation. Results and DiscussionWe begin with the product manipulation check. Results indicate that the sports shirt attained statistically higher means on inspection depth relative to the portable cell phone charger (M = 2.42 vs. M = 1.64; t(409) = 6.88, p < .001). Testing H 1Figure 3 shows mean repatronage intentions by treatment. The 2 × 2 interaction analysis using analysis of variance demonstrates that both deep product (F( 1, 407) = 6.94, p < .01) and store condition (F( 1, 407) = 6.18, p < .05) positively contribute to higher repatronage. Central to our prediction, the deep × store interaction also positively contributes to higher repatronage (F( 1, 407) = 4.82, p < .05). A planned contrast indicates the deep/store treatment clearly evoked the highest repatronage intentions among the three other conditions (Mdeep/store = 5.38 vs. Mdeep/online = 4.88, Mshallow/store = 4.86, and Mdeep/online = 4.83; F( 3, 407) = 6.09, p < .001), consistent with H1. Web Appendix Tables 11.2A–D provide additional details of the analysis of variance and contrast tests.Graph: Figure 3. The effect of first product/channel purchase combination on repatronage (Study 2). Testing H 2We measured the extent to which subjects reflected on the physical engagement component of the experience by analyzing how they articulated the experience in their written descriptions. We recruited two research assistants blind to the research agenda to rate a physical engagement variable, ""try_touch,"" from each respondent's description. We instructed the research assistants to read each description of the customers' shopping experiences. We told the research assistants that some shopping experiences involve elements of touching, trying on, and feeling and instructed them to code try_touch as 1 if the description contains words, synonyms, or themes related to ""try,"" ""touch,"" or ""feel""; alternatively, we instructed them to code try_touch as 0 in the absence of these themes. Intercoder correlation was.92.The following are examples of subjects' descriptions that clearly suggest physical engagement:I would look at the size of it. I would feel its texture. I would test it out. I would see how it would look on me. I would see if my favorite team is on the shirt.I would go into the store and do quite a bit of browsing first. I would allow myself extra time in this store as it is my first time going. I would familiarize myself with the brand and touch everything to test out its quality.I would go into the store and browse shirts. I might try on the shirt before buying it, unless I was sure it would fit and/or I didn't want to spend extra time. Then I'd buy it.We tested the proposed ELT mechanism using try_touch to measure physical engagement. We used Preacher and Hayes's PROCESS Model 83 ([12]) for moderated serial mediation, which combines serial mediation (Model 6) and moderated mediation (Model 7). Following our theory, we used store versus online as the ""X variable"" moderated by deep versus shallow product (the ""W variable"").[16] PROCESS generated estimates and standard errors via bootstrapped sampling with 5,000 iterations. The ELT-based mechanism we propose (deep/store purchase → physical engagement → learning → repatronize retailer) is the ""indirect effect"" reflecting the mediating role of physical engagement and learning on the relationship between a deep/store purchase and repatronage intentions.We first conducted several preliminary analyses to demonstrate the value of moderation and mediation. We first regressed store on repatronage, which yielded a significant direct effect (bstore = .273, p = .013). Then, we added the deep/shallow moderator to this model, yielding a significant interaction effect of deep × store (bdeep × store = .472, p = .029) but rendering the main effect of store insignificant (bstore = .031, p = .84; bdeep = .047, p = .76). Similarly, the direct effect of store becomes insignificant once try_touch and learning are added as regressors (bstore = −.143, p = .146; btry_touch = .425, p < .001; blearning = .442, p < .001).The results of Model 83 regarding moderated serial mediation show that the direct effect of store on repatronage is insignificant (−.143, 95% confidence interval [CI] = [−.336,.0501]). The index of moderated mediation, however, is.188 (95% CI = [.100,.293]), supporting the moderating effect of deep versus shallow product on the impact of the store versus online on repatronage. For the deep product, the conditional indirect effect for store on patronage is.219 (95% CI = [.122,.332]); for the shallow product, it is significantly weaker at.031 (95% CI = [.011,.059]). The results suggest that both the proposed moderation and mediation are at work and that store purchase increases patronage through physical engagement and learning, more so when purchasing a deep product. This confirms H2's prediction that ELT contributes to the mechanism translating deep/store purchasing into repatronage.We conducted two robustness checks to reinforce this analysis. First, recall that the deep/store condition stands out and the other three essentially are equal. We therefore created a deep/store dummy variable equal to 1 if the subject was in the deep/store condition and 0 otherwise. Although regressing deep/store on repatronage yields a significant direct effect (b = .524, p < .001), the direct effect becomes insignificant after we account for the proposed mediation. Serial mediation analysis (PROCESS Model 6) yields a significant indirect effect: deep/store → try_touch → learning → repatronage. Details are in Web Appendix W11 (Table W11.3).Second, we reran Models 83 and 6, switching the order of try_touch and learning, estimating an alternative process: store → learning (moderated by deep product) → try_touch → repatronage for Model 83 and deep/store → learning → try_touch → repatronage for Model 6. While the global model fit and the total indirect effects are unsurprisingly the same across both orderings ([42]), the indirect effects of serial mediation are quite different. For instance, our proposed ordering yields.1882 (SE = .048) for the deep products' index of moderated mediation in Model 83; the alternative ordering yields an index of.0138 (SE = .009). Model 6 highlights this difference more saliently. Whereas the proposed ordering yields a serial mediation indirect effect of.1258 (SE = .03), or 30% of the total indirect effect, the alternative ordering yields the serial mediation indirect effect of.0154 (SE = .006), which translates to 3.7% of the total indirect effect. We acknowledge that [42], p. 698) cautions against trying to infer the correct mediation order by the aforementioned tests. He advocates that the ordering be based on ""strong evidence from logic, theory, and prior research that the hypothesized casual direction is more plausible than indicated alternatives"" ([42], p. 697). We believe the application of ELT we used to specify the try_touch → learning ordering satisfies this requirement.In summary, Study 2 replicates Study 1's finding that the deep/store purchase combination produces the highest repatronage, in support of H1. It also tests the ELT mechanism as proposed by H2. The moderated serial mediation results, and the robustness checks, are consistent with the ELT mechanism. Study 3: Replicating H 3 and Testing Product Generalization (H 4)Study 3 tests our predictions related to generalization of learning. We aim to replicate the HMM's support for H3, which predicts that purchasing deep products in-store increases the likelihood of purchasing deep products online, compared with purchasing shallow products in-store. Further, this study tests H4, which proposes that the impact of purchasing a specific deep product in-store generalizes to related, adjacent deep products. As noted previously, generalization is an important component of experiential learning and could be very powerful for retailers. It demonstrates the temporal interplay between offline and online channels and suggests that deep/store purchase of a particular product ""spills over"" to higher likelihood of purchasing adjacent deep products online when the customer repatronizes the retailer. MethodStudy 3 is a two-treatment between-subjects design. The two treatments were deep product/in-store and shallow product/in-store. We randomly assigned 414 participants from Qualtrics Consumer Panel to the two treatments. To ensure that participants were relevant for our context, we asked Qualtrics to screen them based on age (between 20 and 60 years old), household income (minimum of $30,000; 50% of sample needs to have at least $60,000 household income), and e-commerce experience (need to have purchased a product online at least once in the past six months).We first told subjects that a new sports and outdoor-gear retailer has opened in town. We then asked them to imagine their first shopping trip to this retailer's physical store, where they purchased either a sport shirt (deep product) or a battery charger (shallow product). We then asked them how likely they would be to purchase each of four products from the retailer's website in the future. These four products included two deep products, a sport shirt and a sweater (a product adjacent to the shirt), and two shallow products, a battery charger and an activity tracker watch (neither of which are adjacent to a shirt). Finally, as in Study 2, near the end of the study, after the key measures were collected, we asked the respondents to rate the shirt or the battery charger for product inspection depth, depending on their assigned conditions, to enable us to check the deep versus shallow product manipulation. Details of the questionnaire are in Web Appendix W12. Results and DiscussionManipulation checks confirmed that the shirt was perceived to be deeper on the four-point product inspection depth scale than the charger (shirt = 2.26 (SE = .09), charger = 1.65 (SE = .07); t(412) = 5.51, p < .01).Figure 4 shows that first purchasing a shirt offline increases intentions to subsequently purchase a shirt online, compared with if the first purchase is a charger offline (Moffline_shirt = 4.35, SE = .14 vs. Moffline_charger = 3.62, SE = .13; t(412) = 3.7, p < .01), confirming H3.Graph: Figure 4. The effect of first deep product in-store purchase on future online purchase likelihoods, compared with shallow product purchase (Study 3).We next tested for spillover. H4 posits that generalization will be to adjacent products. Indeed, the results show that the shirt treatment leads to a higher online purchase likelihood for the adjacent sweater than does the charger treatment (Moffline_shirt = 3.88, SE = .14 vs. Moffline_charger = 3.45, SE = .13; t(412) = 2.3, p < .05). This supports H4. While spillover extends from shirt to sweater, it does not extend to the charger and watch—two shallow, nonadjacent products. The likelihood of buying a charger online next time is nonsignificant between the two treatments (Moffline_shirt = 4.48, SE = .15 vs. Moffline_charger = 4.88, SE = .14; t(412) = −1.9, p > .05). Similarly, the difference between purchasing an activity tracker online subsequently is nonsignificant (Moffline_shirt = 4.4, SE = .14 vs. Moffline_charger = 4.5, SE = .14; t(412) = −.5, p > .05). In conclusion, we have support for H4: buying deep products in-store generalizes to adjacent products. Additional regression analyses corroborate these findings and are in Web Appendix W12.In summary, Study 3 demonstrates that buying deep products in-store encourages customers subsequently to purchase deep products online more than does buying a shallow product in-store, in support of H3. Furthermore, buying a shirt in-store increases the likelihood of not only buying a shirt online in the future but also buying an adjacent product (a sweater) online. This supports H4. Study 3 overall testifies to the value of ELT as a theory for understanding the impact of deep/store purchases on future customer value. General DiscussionWe set out to study the role of the physical store in today's multichannel retailing environment. Our thesis was that the physical store increases customer value by providing physical engagement when customers buy deep products. We drew on ELT to formulate four hypotheses: H1 suggests that deep product/in-store purchases increase customer value more than any other product/channel combination. H2 suggests that ELT provides a mechanism that contributes to this effect. H3 posits that customers who purchase deep products in-store are more likely to purchase online from the retailer in the future. H4 proposes that customers will repurchase not only the original deep product but also related, adjacent deep products online. H1 and H2 follow because the store delivers a tangible, concrete, multisensory experience, which facilitates the physical engagement beneficial for buying deep products. This precipitates effective experiential learning. H3 and H4 follow from the generalization phenomenon proposed by ELT.We evaluated these four hypotheses using an HMM applied to field data (Study 1) and two lab tests (Studies 2 and 3). The results support the following conclusions: ( 1) purchasing deep products in-store increases future customer value more than any other product/channel combination (H1), ( 2) the ELT mechanism (deep product/in-store purchases → physical engagement → favorable learning → repatronize the retailer) contributes to this increase (H2), and ( 3) purchasing deep/in-store increases the likelihood of purchasing the focal and adjacent deep products online in the future (H3 and H4). Research ImplicationsOur work has several implications for future research. First, researchers should consider the product/channel combination in studying consumer decisions in a multichannel context. We focus on the physical store and deep products, but the bigger picture is for future research to study product and channel choices together. Not doing so may incur a lost opportunity—the insights in our article would have been diminished if we had focused solely on channel or product.Second, the product inspection depth concept extends theory regarding product categorizations such as digital versus nondigital by incorporating details related to both physical and visual inspection. We believe this is a valuable concept for studying and comparing products because inspection depth varies appreciably across products, and channels differ in the degree of inspection they can provide. We hope researchers will apply and perfect this concept.Third, we demonstrate the applicability of ELT to developing customer relationships. This importantly extends the domain to which ELT has been applied. The pivotal role of the customer experience ([60]) cannot be understated.Fourth, our reliance on ELT proved fruitful, but [25] posit that decision making can be a dual process, drawing on cognitions and feelings. ELT prescribes an involved process of reflection, hypotheses, and repetition, by which consumers form cognitions. ELT does not directly tap feelings. Under this umbrella are concepts such as trust, commitment, affect, and emotions advanced by [34] and [ 5]. These concepts provide additional theories for our results and thus need testing. Managerial ImplicationsStudy 1's data are from 2005, yet our findings are in evidence today. The last decade has witnessed a marked increase in the opening of physical stores by online retailers, despite myriad changes in the retailing environment. This attests that our findings are not ephemeral. The general lesson of our research is for retailers to create a concrete, tangible, and multisensory experience for customers buying deep products. This sets the stage for favorable experiential learning and increased customer value. Retailers can do this in numerous ways:First, when retailers find that a customer is buying online but is decreasing in value, we suggest a promotion for deep products in-store. Our marketing simulations show that there is potential to increase customer value through direct marketing. Second, retailers should facilitate physical engagement for deep products through merchandising and training sales personnel to walk customers through the engagement (e.g., by helping customers try and use deep products in-store). Third, retailers cannot and should not infer product inspection depth solely from predefined product categories, because there is much variation in inspection depth with a particular category. Rather, management should infer inspection depth using our proposed measures or expert, independent judges. Fourth, we recommend retailers use a deep/offline onboarding strategy for new customers. They should use acquisition channels and product promotion strategies that encourage the first purchase to be deep products in-store.Our general lesson applies to recent developments in retailing. For example, showrooming ([10]) starts with customers in-store, where the retailer can provide physical engagement. However, the retailer may lose customers who use their smartphones in-store to find the product elsewhere. There are two possible solutions. Retailers can train sales reps to attend to customers buying deep products and equip reps with a mobile device to place orders, or retailers can provide customers with an app to ""lock them in."" Interestingly, [55] find that store-oriented customers are good targets for retailer apps. Similarly, ""buy online, pickup in-store,"" a form of ""web-rooming,"" can get customers to the store where they can physically engage with additional products to the ones they ordered online.Our central thesis also has implications for retail loyalty program design and its real-time management. Loyalty programs may be more effective if they provide incentives for customers to shop in-store, such as extra reward points for in-store shopping, particularly when it comes to deep products. These programs need to provide the data, system, and incentives needed to route customers to the physical store when needed, such as when customer value is waning. LimitationsOur work has limitations that suggest opportunities for future investigation. First, our hypotheses assume that physical stores provide effective physical engagement and favorable experiential learning. Our results could have turned out differently if our focal retailer's stores did not provide satisfactory experiences. This meant that our hypotheses were nontrivial and falsifiable, and future work should investigate the store features that best provide physical engagement. Second, we could not observe and thus could not incorporate customers' category expertise or preference evolution due to product consumption. Two customers who bought similar tents could have different camping experiences, which would partially determine future purchases that are outside of the firm's control. Third, with transactional data typical in customer relationship management research, we assumed that customers make channel and product decisions jointly. Future work using more granular data could examine the sequence of channel and product choices and explore situations such as planned versus serendipitous purchases. Fourth, we do not know exactly when a customer makes the decision of what and where to purchase. A shopping list study would be useful for future research. Finally, our observational data were from one retailer—a specialist in outdoor products. Future work should consider other product categories. Future DirectionsGuided by our mantra to create physical engagement to enhance customer value, we next discuss additional fertile areas for research. Store or showroom?Is the traditional physical store, with its requisite square footage and inventory, the best way to sell deep products? As fulfillment logistics have gotten faster, more orders can be placed via in-store kiosks and staff-assisted online ordering and can be fulfilled quickly. This suggests stores do not need to allocate a large space for inventory and can use that real estate to help convert the physical store to a showroom. The question is which is best—physical store or showroom?—in terms of customer preferences and the financials. Full or limited assortment?As the physical store's value lies in facilitating physical engagement through deep products, stores may not need to carry a large assortment (not all sizes and not all colors). The counterargument is that stores should carry a broad assortment because customers might want to physically engage with specific sizes/colors. Future research should study and resolve this tension. Full or limited staff?A trained and empathetic sales team would play a key role in delivering physical engagement to the customer. We noted this in discussing showrooming, where the sales rep was needed to keep the customer ""on course"" with the retailer. [20] also delineate an important role for staff in developing customer value. This would suggest full staffing of physical stores (see [10]). However, this strategy is expensive, and customers may be quite capable of physically engaging on their own. The role of private labelAs noted in formulating H1, we assume the favorable learning the customer gains from physical engagement transfer to the store. One way the retailer might ensure this is to emphasize its private label. Nordstrom, L.L.Bean, and Warby Parker are good examples. Leveraging technology to create physical engagementPhysical engagement is a particular challenge for online retailers. What combination of videos, chat, user testimonials, virtual reality, augmented reality, and other interactive features should be deployed to mimic in-store physical engagement?Although the current state of augmented reality technology is probably not realistic enough to fully capture physical engagement ([ 6]), future work could examine how this technology can satisfactorily do so for certain product categories, consumption contexts, and consumer segments.One recent notable example is the virtual tasting experiences offered by Wine.com, an online wine retailer. This program encourages customers to order a featured wine ahead of time, then go online and, either live or via recorded videos, taste the featured wine alongside its winemaker or renowned critics such as Steven Spurrier. This program not only can result in immediate sales of the featured wines and fills the vacuum created by the closure of physical tasting rooms during COVID-19 but, according to our theory, also has the potential to facilitate sensory engagement (in this case, visual, audio, taste, and smell). Research could investigate whether such creative digital efforts can translate into long-term loyalty towards the online retailer. Identifying physical engagement-prone customersRetailers might consider updating their customer segmentation schemes to reflect customers' needs for physical engagement. [36] show that the need for haptic (touch) experiences is an individual trait. An interesting line of future research is to investigate whether this trait evolves over time and what drives this evolution. Retailers could thus explore ways to identify physical engagement-prone customers and design their stores and websites accordingly. Other forms of physical engagementRetailers may want to explore other avenues to physically engage customers in-store, such as through cultural events or prosocial efforts. For example, they may celebrate ethnic holidays and dedicate a certain percentage of sales to charitable causes. Can these be turned into forms of physical engagement? A retailer can advertise online its efforts to fight COVID-19, but it can also demonstrate in-store the personal protective equipment its donations buy for the community. Future work could investigate whether these various in-store efforts generate customer interactions and affect that enhance customer value.These speculations, along with the more direct implications stated previously, demonstrate the richness of marching to a simple yet powerful message: Retailers can increase customer value by providing physical engagement when selling deep products. We hope both researchers and practitioners will leverage this message in future work. " 31,Identifying Market Structure: A Deep Network Representation Learning of Social Engagement," With rapid technological developments, product-market boundaries have become more dynamic. Consequently, competition for products and services is emerging outside the product-market boundaries traditionally defined based on Standard Industrial Classification and North American Industry Classification System codes. Identifying these fluid product-market boundaries is critical for firms not only to compete effectively within a market but also to identify lurking threats and latent opportunities outside market boundaries. Newly available big data on social media engagement presents such an opportunity. The authors propose a deep network representation learning framework to capture latent relationships among thousands of brands and across many categories, using millions of social media users' brand engagement data. They build a brand–user network and then compress the network into a lower-dimensional space using a deep autoencoder technique. The authors evaluate this approach quantitatively and qualitatively and visually display the market structure using the learned representations of brands. They validate the learned brand relationships using multiple external data sources. They also illustrate how this method can capture the dynamic changes of product-market boundaries using two well-known events—the acquisition of Whole Foods by Amazon and the introduction of the Model 3 by Tesla—and how managers can use the insights that emerge from this analysis.","Firms compete in a market to satisfy the specific needs of consumers in the market. The market and the competing products make up a ""product-market,"" with the boundary defining the brands competing within that market. Market structure is defined on the basis of these product-markets and their (possibly overlapping) boundaries. Identifying the product-market boundary and examining the strength of competition between brands within the product-market have long been important issues with strategic implications for next-generation product design, product positioning, new customer acquisition, and pricing and promotion decisions. Rapid changes to the competitive environment, however, have made identifying the product-market boundaries increasingly challenging. With technological advances, the product-market boundaries themselves are changing and competitive threats and opportunities are emerging outside of narrowly defined product-market boundaries.Numerous recent competitive events support the idea that product-market boundaries are highly fluid. For example, the digital camera product-market was upended by technological developments in smartphone categories. Similarly, Tesla, which initially entered the product-market of high-end automobiles with an innovative fuel technology, has since rolled out products for the lower-end market, thereby changing competition in the lower-end product-market as well. Amazon, previously an online platform, essentially crossed product-market boundaries when it acquired Whole Foods and entered the offline product-market. In many such situations, product-market boundaries based on traditional Standard Industrial Classification and North American Industry Classification System codes are inadequate indicators of emerging threats and opportunities. Given the potential for new and unforeseen relationships between brands, managers need deeper insights into the fluid product-market boundaries to be able to spot potential competitors and complements, identify cross-promotion strategies, and develop firm-level strategies.These observations naturally lead to several important questions: How can managers accurately identify potential threats and opportunities? If a competitive threat emerges from a different market, how can managers proactively anticipate such threats? How can we answer these questions and derive marketing insights using easy-to-obtain and publicly available data? Our article aims to answer these questions using large-scale (>100 million) social media user engagement data (likes and comments) spanning several thousands of brands in different product/service categories.Over the years, academics and practitioners have contributed significantly to developing various methods to define and identify market structure (see the review by [46]]). These include survey-based methods such as brand concept maps ([19]) and the Zaltman metaphor elicitation technique ([53]), methodologies based on observational purchase data (e.g., brand switching) ([21]; [37]), consideration sets ([42]), and scanner-based purchase data ([10]; [36]; [45]). Within the online context, researchers have used unstructured user click streams ([32]), online search logs ([24]; [42]), and customer reviews ([26]). Many of these methods use data from the bottom of the purchase funnel, such as evaluation- and purchase-stage data, and thus assume that the product-market boundaries are prespecified. Even those methods that use data from the top of the funnel at the awareness or preevaluation stage, such as forum discussions ([35]) and hashtags ([33]), define a product-market boundary first and then examine the competition within the prespecified product-market to make these methods implementable. Thus, many of the methods are unable to capture changes to the product-market boundaries and/or the impact that a brand from outside the boundary may have on brands within a product-market.Our methodology creates a more inclusive representation of brands by examining brand–user relationships at the top of the purchase funnel. Unlike the extant methods for identifying market structure that use data from consumers' lower funnel activities such as purchase data, brand switching, price comparison data, or consideration data that prespecify boundaries (e.g., [10]; [21]; [37]; [42]; [50]), we use upper-funnel user–brand engagement data (such as liking and commenting on brand posts) from social media that spans product-markets. At the lower end of the purchase funnel, consumers winnow down the brands they consider to a few substitutes. Thus, interactions at this stage are not as informative of the broader (and possibly complementary) linkages between the brands across product-markets, which are captured more easily at the upper funnel. For example, a consumer considering travel may consider hotel or Airbnb options, airline options or travel intermediaries. At this early stage (the upper end of the funnel), understanding such user–brand linkages could be more informative of the broader relationships between the brands on a continuum from substitutes to complements. Our methodology uses such upper-funnel user–brand engagement data to identify these latent relationships among a large number of brands.Many extant studies in market structure, including those mentioned previously and those using big data technologies (e.g., [ 7]; [12]; [26]; [35]; [42]), view the competing/complementary brands as brand–brand networks. That is, they specify the relationship between any two brands using similarity metrics derived from brand switching, co-occurrences, and word embeddings, without directly modeling the entities (customers, individual consideration sets, or individual reviews) that give rise to such similarities. Our methodology based on brand–user networks considers both brands and users as primitives and uses as input the relationship in terms of each user's liking and commenting on brands. The essential difference between these approaches and our methodology is that extant research considers aggregate data of relationships between brands (brand–brand) as input, whereas our methodology considers the disaggregate individual-level relationships between users and brands (brand–user) as input.The distinction becomes more salient when a product-market boundary is not prespecified. Consider, for example, User 1, who likes United Airlines and Hyatt, while User 2 likes Southwest Airlines and Hyatt. When the product-market is prespecified as ""airline brands,"" information about the users liking the Hyatt brand is discarded. As a result, information that could provide insights into the relationship between United Airlines and Southwest Airlines through their relationships with Hyatt is not considered. However, when we do not prespecify the product-market boundaries, we are able to leverage all such information and create a more accurate representation of the brands.From this premise, we first construct a large-scale brand–user network based on user engagement on brands' social media public fan pages. Then, we propose a deep network representation learning method to discover relationships within the data. Specifically, we use a deep learning method suitable for ( 1) handling large data efficiently and ( 2) learning complex patterns from data effectively (see [ 2]; [49]). The process leads to a low-dimensional representation (i.e., a vector) for each brand and each user by training a deep autoencoder on the network data. The deep autoencoder is similar to traditional dimensionality reduction methods such as principal component analysis (PCA) in capturing latent factors in data with few dimensions. It is, however, very different from those methods in that it uses a nonlinear transformation function to learn the latent patterns in data while reducing the noise in the data. In our context, the deep autoencoder can preserve the first-order (user–brand direct connection) and the second-order (two users connecting to the same brand, or one user connecting to two different brands) network topologies. As a result, brands with network structural equivalence are located closer together in the representation space, while brands with dissimilar network structures are located further away from each other. This method also projects users and brands onto the same dimensional space, which can be used for many different follow-up analyses. We use an illustrative example (in Figure 1) to demonstrate how network representation learning works.Graph: Figure 1. An illustration of deep network representation learning.Suppose we have three brand nodes (B1, B2, and B3) and five user nodes (U1, U2, U3, U4, and U5) in a network. Our network representation learning approach aims to find a function that maps each node into a low-dimensional vector (e.g., three dimensions, for the sake of illustration) while the network structural information is preserved maximally. That is, when nodes exhibit similar structures (first order and/or second order), they are projected onto similar vectors and located closer in the reduced three-dimensional embedding space. Because U1 engaged with B1, we expect the vector representation of B1 and U1 to be close. Similarly, B2 is closer to B1 than to B3 because B2 shares more common users with B1 than with B3. Because B2 has connections to U4 and U5, this makes B2 lean toward them.We establish the face validity of our approach through the identification of product-market boundaries. Our analysis of the brand–user engagement data of over 5,000 brands and nearly 26 million users reveals product-market boundaries with high face validity—grouping of specific categories, high-end brands, and overlaps. We then conduct external validation checks using additional sources including survey and Google search trend data. The market structure derived using our approach is highly correlated with those derived using external data sources. Our approach also overcomes common limitations in extant methods such as data sparsity. Our event studies on Amazon's acquisition of Whole Foods and Tesla's introduction of the Model 3 illustrate how our methodology captures the changes in product-markets associated with these events. We also discuss how the market structure maps can reveal opportunities and threats facing a brand. For instance, our market structure identifies Disney Cruise Line and Hyatt—two brands outside the airline market—as proximal brands to Southwest Airlines. Such findings provide opportunities for Southwest, as it can target those who like Disney Cruise and Hyatt in social media, cross-promote its brand by teaming up with Disney Cruise and/or Hyatt on each other's websites, or launch coalition loyalty programs.Our article contributes to product-market research by leveraging the information embedded in big data of user–brand engagement networks to identify product-markets without having to prespecify boundaries. User–brand engagement network data at a high level in the purchase funnel (interest phase), together with deep learning techniques, provide us with insights at a greater scale and level of detail than extant methods. Our ability to map a large number of brands and precisely visualize brand relationships using learned vector representations enables managers to identify opportunities and threats that lie beyond product-market boundaries. Moreover, our method satisfies the three elements widely regarded as essential to successful real-world applications of artificial intelligence: data, algorithm, and computing power ([ 2]). In this article, we leverage deep learning and a network representation learning (algorithm) to understand market structure using large-scale social media data (data). This model implementation is efficient under NVIDIA P100 graphics processing unit, with Tensorflow as the backend framework (computing power). In summary, our study is an apt illustration of how artificial intelligence can be used to tackle a traditional marketing problem and provide richer insights for mangers in a rapidly changing competitive environment. Background and PositioningExtant work in identifying competitive market structures dates to the 1970s (e.g., [ 8]; [20]), when diary panel–based brand-switching purchase data and survey-based consumer judgments of substitution in use or similarities were used to construct market structure maps. These studies depended on customer data generated either at a late stage of the customer journey or at the very beginning of the journey. The increased availability of scanner-panel data of purchases, market structure models with marketing mix (e.g., [ 5]; Kannan and Wright 1991), and dynamic market structure models (e.g., [10]) provided more detailed insights into interbrand relationships and competition. Approaches such as brand concept maps ([19]) and the Zaltman metaphor elicitation technique ([53]) relied on data collected using surveys and, therefore, were effort intensive. Given the scaling issues with the maximum likelihood–based models and the limitations of survey data, the product-market boundaries were prespecified generally at the industry level so that a smaller number of brands within an industry could be analyzed. The advent of online sources, such as review platforms, social media platforms, and clickstream data, dramatically increased the volume and variety of data for market structure studies, especially at the awareness, search, and consideration stages of the customer journey ([24]; [26]; [35]; [42]; see Tables 1 and 2). Even with a large volume of data, these studies predefine the product-market boundaries at the industry level to make the analyses viable.GraphTable 1. Comparison of Different Types of Work on Market Structure Discovery. GraphTable 2. Summary of Difference Among Extant Literature on Market Structure Discovery. 1 Notes: NLP = natural language processing; ML = machine learning; N.A. = not applicable.There are other studies where the product-market boundaries are not predefined: [11] using online reviews, [33] using social tags, and [ 7] using Twitter hashtags. More recently, using word embeddings, [12] analyze customers' market baskets of items purchased on shopping trips. Still, from a methodological perspective all these studies use brand–brand networks—a distinct disadvantage, as we discussed previously. Our methodology uses brand–user networks, and the scale at which we analyze the data is much larger than any of the extant methods (cf. [12]). Social Media EngagementWe analyze social media engagement data in the form of user–brand links. Social media platforms such as Facebook, Twitter, and Instagram host public fan pages created by firms to facilitate communication with customers and promote products. The user–brand engagement could be in the form of a user liking a post by the brand, sharing a brand post, or commenting on a brand post. Because each of these likes, shares, and comments/posts is a user–brand link in our study, it is important to understand what they represent. Surveys of fans of brands have revealed many reasons as to why users ""like"" a brand or post/share comments. Positive motivations for interacting with a brand include to support a brand they like, to get a coupon or discount, to receive regular updates from the brand, to participate in contests, to share personal experiences, to share their interests/lifestyles with others, to research brands, to imitate a friend who likes the brand, or to act on a recommendation from another fan ([25]; [34]; [39]; [41]). Conversely, users may also leave negative comments to hurt a brand in favor of its rival brand ([17]).In our approach, we make a minimal assumption by creating a user–brand link regardless of the type of engagement (like, share, or comment/post). This assumption is based on the rationale that users interacting with a brand online exhibit their interest toward the brand to some extent. Thus, the two brands are related to one another on a spectrum ranging from substitutes to independents to complements. Prior research has examined such contexts and studied the impact of user engagement on brand image and customer purchase intentions with mixed results ([16]; [34]). [31] use a field experiment to find that users who liked a gym brand online were likely to become members of that gym offline. In another field experiment setting, [18] find that ""liking"" is simply a symptom of a positive brand attitude and does not imply the fan is any more loyal to the brand or any more likely to purchase the brand. In addition, it is only when users who liked the brand are targeted using promotional communication by the firm that purchase probabilities increase. Thus, for our research purposes we treat a like or a comment/post as exhibiting an interest toward the brand at the beginning of the customer journey. Such a tendency for users to connect to brands is generally interpreted as interest and may indicate broader (e.g., offline) interactions ([ 7]; [25]; [34]; [35]), which is consistent with our treatment. Our proposed approach is also consistent with research in social network analysis suggesting that social network structure equivalence reflects value/interest homophily and can be used to measure social proximity ([29]). MethodologySocial network platforms, such as Facebook, Instagram, and Twitter, can be abstracted as a network containing business (firm) accounts and individual user accounts. Firms use the public fan pages of business accounts to communicate with their customers and fans. Users interact with brands and with each other in different ways, such as commenting, liking, sharing, and following. To discover latent relationships among brands, we propose a deep network representation learning framework with the following steps. Step 1: Data collectionWe specify a set of brands that is of interest in the social network platform. We then download all available user engagement data from the brands' public fan pages covering an appropriate time window based on managerial interest. A user engagement is defined as either liking or commenting on a firm's post on its public fan page. Note that for the sake of privacy, we do not attempt to collect any personal information of users. Rather, the only user information we obtain is the unique user identifier, assigned by platform, and the user's public engagement activities, consistent with recent studies on social media marketing ([17]; [23]).[ 5] Moreover, different platforms may have their own specific data policy. For example, Facebook does not permit collecting personal information from individuals who liked a given page. Such data restrictions and potential ethical concerns do come at a research cost, as we are unable to verify how representative they are of the population at large. Step 2: Network constructionWe start with a cleansing operation to remove spurious users. We then construct a brand–user network including all selected brands and all users engaging with them. A brand node and a user node are connected if the user engages with the brand. The strength of an edge between a brand node and a user node is the engagement frequency. Step 3: Deep network representation learningThe deep network representation learning algorithm represents each node (brand or user) as a low-dimensional vector, also known as a node embedding. Embedding techniques are not new in marketing. For example, [49] adopt pretrained word embeddings, where each word is represented as a low-dimensional vector, to extract insights from textual reviews. However, our node embeddings are trained via an unsupervised deep autoencoder. This representation learning is essential to data-driven analysis, and the learned low-dimensional embeddings are useful for the downstream task of identifying and visualizing the product-markets.The objective in using an autoencoder is to learn the representation of the data so that each node can be represented in a lower-dimensional space while the network structure between users and brands is preserved. It trains the network to ignore the ""noise"" in the data and focus on the primary latent structure. The autoencoder reduces the dimensionality of the input data to a ""bottleneck"" (the reduced encoding) and, using the reduced encoding as input, reconstructs a representation of the original data. Learning occurs through backpropagation of the loss (see detailed definition in Web Appendix WA1) to achieve a reconstructed representation as close as possible to the original representation. We are interested in the bottleneck-reduced encoding for developing market structure. In essence, we can compare the dimensionality reduction functionality of the autoencoder with that of PCA. Whereas in PCA the reduced dimensions are linear combinations of the input variables, the reduced dimensions in autoencoder are nonlinear and nonorthogonal, which is achieved through nonlinear activations of the neurons, allowing the model to learn more powerful generalizations than PCA can.In our application, the autoencoder works on the large brand–user network in an attempt to preserve the network structure such that ( 1) nodes that are directly connected have similar vectors (are closer to each other) in the reduced embedding space, and ( 2) nodes that are not directly connected but share structural equivalence (such as many common neighbors) are also similar in the embedding space. These two types of similarity are referred to as the first-order (direct connection) similarity and the second-order (network structural equivalence) similarity. Formally, we denote the aforementioned network as G=(Vb,Vu,E) , where Vb=(v1b,v2b,...,vnb) represents a set of n brand nodes, Vu=(v1u,v2u,...,vmu) represents m user nodes, and E={ei,j},i≤m,j≤n represents all links between users and brands. ei,j indicates an engagement between user i and brand j . Given such a network G , the network representation aims to learn a mapping function f:vib,vju↦wib,wju∈Rd , where d≪min(m,n) . wib,wju are called brand embedding and user embedding, respectively. A commonly used embedding dimensionality d is 300 ([30]; [40]). The objective of the mapping function is to develop appropriate embeddings so that the brand proximities, brand–user proximities, and user proximities exhibited in the original network are preserved as much as possible in the reduced embedding space. (Technical details of the autoencoder methodology and parameter tuning are discussed in Web Appendix WA1.) Representing brands as dense low-dimensional vectors allows us to capture brand relations from multiple facets, as opposed to using unique vectors for each user and each brand as in a network adjacent matrix representation. An example is illustrated in Figure 1. Step 4: Market structure discoveryDrawing on vector representation for brands and users, we use learned embeddings to efficiently compute similarity among brands and to visualize natural clusters of related brands. Finding similar brands to a focal brand can be achieved by a nearest-neighbor search based on the widely used cosine similarity, which measures the cosine of the angle between two vectors and has a range [−1, 1]. Visualizing natural clusters of related brands can be achieved by a dimension reduction method, such as t-distributed stochastic neighbor embedding (t-SNE; [30]), which projects high-dimensional data into a low-dimensional space (e.g., two or three dimensions).[ 6] It has been used for visualization in a wide range of applications and is especially well-suited for visualizing high-dimensional representations learned from deep neural networks. t-SNE preserves the distance of data points well, such that data points nearby in a high-dimensional space (d = 300 in our case) would be close in a lower-dimensional (e.g., two-dimensional) space, while distant data points would be further apart in a lower-dimensional space. Thus, we observe that related brands are surrounding each other in the reduced two-dimensional space after t-SNE. DataWe use Facebook as our empirical benchmark, as it is one of the largest and most representative online social network platforms. (Our model can be generalized to other similar social network platforms.) To collect Facebook data, we first obtain a list of U.S. brands with the most followers from the social media marketing website Socialbakers.[ 7] Facebook public fan pages are categorized into several groups on Socialbakers, such as Brands, Celebrities, Community, Entertainment, Media, Place, Society, and Sport. We focus on the ""Brands"" category because it covers a wide range of industries and is more interesting to marketers. On Facebook, every brand is associated with a category chosen from the predefined Facebook option when creating the public page. This category label is solely determined by the brand and is aligned with its core business (e.g., Walmart is in the category of ""retail,"" Amazon is in the ""ecommerce"" category). In total, we obtain 5,478 different brands, covering 25 different categories. The largest brand, in terms of number of followers, is Walmart, with 30 million followers. The smallest brand is Bladz Jewelry in the ""fashion"" category, with 100,000 followers. Figure 2 shows the histogram of number of followers of brand Facebook page. We observe that the data set contains brands with varying popularity, making it representative of brands on Facebook.Graph: Figure 2. Histogram of number of followers of 5,478 Facebook brands.On Facebook, firms post on their public fan pages and allow users to comment, like, and share posts. The posts become an important marketing channel for businesses to interact with their customers. We use Facebook Graph API[ 8] to download all activities visible on a brand page such as posts by the brand administrator, as well as posts by users, including comments and likes on brand posts. It is worth emphasizing that to ensure privacy protection, we do not download any user profile information or examine the content of user comments. All engagement activities are represented by unique user identifiers, regardless of whether the user has a public or private Facebook profile, and brand identifiers. The data set collected for this study covers the period from January 1, 2017, through January 1, 2018. In total, we obtain 106,580,172 user–brand engagement activities from 25,992,832 unique users. Because prior research has shown that online interaction is a reflection of broader and even offline interaction ([38]), given the scale of user online engagement in this study, we believe it is a good proxy of how the overall consumer population perceives these brands.To ensure data quality and robust results (i.e., that the comments on Facebook brand pages reflect genuine user experiences, opinions, and interactions with brands), we design a set of rules, following [54], to remove fake users and their corresponding activities. For example, we find one user who liked posts across 475 different brands. As most users are likely to be interested in far fewer brands, we remove users who like posts on more than 200 brands, which accounts for.01% of the total users and 1.6% of the total user–brand engagement. We also remove users who posted duplicate comments containing URL links. Table 3 describes the data details. The brands' degree distribution (number of connections) exhibits a scale-free distribution (shown in Figure 3), a well-documented phenomenon in most social networks.Graph: Figure 3. Degree distribution of brands in the user–brand network.GraphTable 3. Data Description and Statistics. Evaluation and ResultsIn this section, we extensively evaluate the market structure derived from our approach from both quantitative and qualitative perspectives. We also validate the derived market structure using two external data sources: consumer survey and Google search trend. Visualization of Market StructureWith the learned brand representation vectors, we can visualize how the brands are grouped and focus on local fine-grained brand proximity. We use t-SNE to obtain market structure visualization by reducing the learned 300-dimensional brand representations to obtain the associated 2-dimensional visualization map. Figure 4 presents the global structure of the brands in our Facebook data. Each data point in the figure denotes a brand belonging to one of the 25 categories, and each category is indicated by a different color. We interpret the visualization as follows: the closer any two brands are in the figure, the more similar their brand representations are in the 300-dimensional space (see Figure 4). The color codes in the map indicate brands in the same Facebook category, with the category label selected by the brands themselves on Facebook.Graph: Figure 4. The global structure among brands.The global Facebook brand market structure map yields several interesting observations. First, there are clear grouping patterns into clusters, particularly between brands in the same industry (points with the same color tend to be in a group). For example, Cluster 1 in Figure 4 (expanded in Figure 5) includes nonluxury domestic and imported automobile brands such as Toyota, Nissan, and Mazda, as well as some automobile accessories brands such as Michelin, DENSO, and Auto Parts. Note that in our data we have several luxury automobile brands such as BMW, Mercedes-Benz, Audi, Tesla, and Maserati, which are not close to the brands in Cluster 1. In fact, they are clustered in a different region of the map with other luxury brands such as Chanel, Gucci, and Cartier. Such a separation between luxury car brands and nonluxury car brands further confirms that brand representation learned from our approach captures latent semantics in multiple dimensions not only on the industry dimension but also on the price and luxury dimensions. The strength of our methodology lies in its ease of capturing these relationships on a single map, which it does by locating thousands of brands in the market structure map and highlighting the complex and possibly overlapping product-market boundaries characterizing these brands. We present a robustness check for different visualization methods in Web Appendix WA5.Graph: Figure 5. Enhanced view of Clusters 1 (top left), 2 (top right), 3 (bottom left), and 4 (bottom right).We provide an enhanced view of the four clusters in Figure 5 to examine the fine-grained local market structures. Panel A displays automobile brands along with automobile accessories and motorcycle brands at the top. Panel B displays premium vacation resort brands, such as The Signature at MGM Grand and the Coconut Bay Beach Resort & Spa. Panels C and D contain airline brands and cosmetic brands, respectively. Taken together, these maps provide face validity to our methodology in terms of core brands making up an industry and the overlaps among product-markets. Identifying Proximal BrandsWhile visual mapping is sufficient to provide a gestalt picture of all 5,000 plus brands in the aggregate, it does not provide the actual distance between the brand vectors in the reduced dimension space. Because identifying proximal brands for substitute/complement analysis is a critical task in marketing decisions ([ 8]), we focus on identifying proximal brands from the perspective of a focal brand. In doing so, we offer a new perspective that reflects the nature of the varied relationships ranging from substitutes to complements in the social network space.In this illustration, we choose United Airlines and Southwest Airlines from the airlines category and Audi USA and Nissan from the automobile category, as these brands are generally regarded as having different consumer bases and belonging to different submarkets. Each of the four brands is referred to as a focal brand, and we find their top ten proximal brands according to cosine similarity. Table 4 provides several interesting insights. First, our method is able to capture specific brand latent characteristics. For example, Southwest Airlines is generally considered a low-budget airline compared with United. The brands most proximal to Southwest Airlines and United reflect this difference. The proximal brands for Southwest Airlines are JetBlue, Frontier Airlines, and Allegiant, while the most proximal brands for United are major domestic and international airlines, such as American Airlines, Delta, Lufthansa, All Nippon Airways, Air China, LATAM Airlines, and Air New Zealand. Similar results also are identified in the automobile industry. Second, we observe asymmetric competition (see [42]). For example, Southwest Airlines is the fourth-most-proximal brand to United Airlines, while United Airlines ranks sixth in the set of top proximal brands to Southwest Airlines.GraphTable 4. Top 10 Proximal Brands to Each Focal Brand. Third, unlike prior market structure analysis, where proximal brands are usually from the same industry as the focal brand, the top most proximal brands derived from our analysis are from different industries. For example, a brand called ""Airfarewatchdog"" is proximal to both United and Southwest Airlines. Airfarewatchdog is a deal-finder for flight tickets and has a large follower base (over 1 million) on Facebook. Traditional market analysis would simply ignore this brand, as it is not an airline. Further, it is also interesting to see that Southwest Airlines is closer to Airfarewatchdog than to United, which may indicate that the fans of Southwest Airlines are more likely to use a deal finder before purchasing flight tickets; thus, Airfarewatchdog could be a complement to Southwest when customers look for cheap flights on that site and end up at Southwest, or it could potentially compete with Southwest. In either case, Southwest could focus more on this site and examine the nature of the relationship. Identifying Opportunities/ThreatsOur market structure map can help managers identify brands outside of the product-market that are close to a specific brand and, thus, identify opportunities and threats posed by different brands. Take the airline product-market (Figure 5, Panel C) as an example. In our analysis, Disney Cruise Line and Hyatt are two brands outside of the airline product-market but are identified as proximal brands to Southwest but not for United. These proximal locations simply are due to a greater number of users in our data set liking both Southwest and Hyatt ( 2,709) versus the number of users liking both United and Hyatt (954). Similarly, a greater number of users like both Southwest and Disney Cruise Line ( 3,050) than like both United and Disney Cruise (729).Such findings can provide opportunities for Southwest, as it could target users who like Disney Cruise and Hyatt on social media. Southwest could cross-promote these brands by teaming up with Disney Cruise and/or Hyatt on each other's websites and launch coalition loyalty programs. From the viewpoint of other hotel chains that are competitors to Hyatt, these could be potential threats, so gleaning such insights early on may help them take proactive actions. Such opportunities/threats are difficult to identify when product-markets are prespecified, and they cannot be obtained easily through other means. Large Brand Versus Small BrandOur user engagement data set contains top 5,478 primarily large brands, ranked by their popularity (number of followers as of data collection period) on Facebook. A key question is whether our proposed approach is still able to identify meaningful market structure for smaller brands. If they can find the right position in the product-market structure, smaller brands have the potential to increase consumer awareness and interest in their brands ([13]), which could lead to a permanent benefit in terms of competitive advantage ([47]). Therefore, to test whether our methodology is able to capture relationships among large brands as well as small and local business brands, we add a set of smaller brands to the original data set. Specifically, we focus on the ""Travel"" category, as it includes many small, local travel agencies, and their followers on Facebook range from a few hundred to a few thousand on average. In total, we have 241 travel brands. Figure 6, Panels A and B, plot the distribution of the number of followers of these travel brands and shows that it is quite diverse.Graph: Figure 6. Size and market structure of 241 travel brands.Upon applying our methodology to the enlarged data set, we observe (Figure 6, Panel B) that these 241 travel brands are predominantly located in two areas. This pattern indicates that the latent brand relationship is well captured, even when brands have few engagement activities due to their smaller user bases. In a brand–brand network, such a small number of shared user bases could result in a failure to capture proximal locations, in essence treating them as noise.The market structure uncovered for these small businesses by identifying their proximal brands has good face validity. For example, ""The Luxury Travel Expert"" is an information portal for luxury travel and premium tours, with about 11,000 followers on Facebook as of our data collection period. Most posts receive fewer than ten comments and likes. The top proximal brands based on the cosine similarity are Smithsonian Journeys, The Peninsula Beverly Hills, Peter Sommer Travels, Quasar Expeditions, and DuVine Cycling. It is noteworthy that these are small travel brands that focus on expert-led, small-group, luxury, and premium tours. The results further confirm that our deep network representation learning method is generalizable to both small and large brands. This analysis also allows brand marketing managers to identify business opportunities. For example, in our analysis, the two brands The Luxury Travel Expert and The Peninsula Beverly Hills are quite close. The former is an information portal for luxury travel and premium tours, and the latter is a five-star luxury hotel. Therefore, the marketing manager of the Peninsula Beverly Hills could promote the brand on the information portal website to attract users from The Luxury Travel Expert to expand its customer base. Within-Industry Market Structure AnalysisExtant methods typically predefine the product-market boundary to derive market structure and brand relationships. In contrast, we allow product-market boundaries to emerge from the data. Therefore, a natural question is whether it is necessary to have a broader range of brands from other industries to derive a highly precise market structure for a specific industry. Although managers would typically focus on engagement data for their brands and for brands within the same industry, how does engagement data from brands in different categories help? To answer this question, we choose the ""auto"" category and only use the engagement data from the auto brands to derive the market structure. In the data set, we have 163 auto brands, including cars and car accessories brands (e.g., tires, oil), with 2.7 million user engagements in total. The analysis shows (Figure 7, Panels A and B) that structures with reasonable face validity still emerge using only the auto brands data. For example, the top left corner in Figure 7, Panel B, presents a cluster of imported auto brands such as Kia Motor America, Toyota, and Nissan. However, compared with the derived auto brand market structure learned from using all brand data, as shown in Figure 5, Panel A, the market structure is less clustered and more ambiguous.Graph: Figure 7. Visualization of market structure of using engagement data only from ""auto"" brands. Notes: The right panel is the zoomed in visualization with BMW as centroid.Next, we compare the market structure using the engagement data from the auto brands alone with that from all brands across categories in a qualitative manner. Specifically, we choose the brand FMF Racing, which is a company that develops dirt bike exhausts for off-road or racing motocross riding. Using the engagement data from the auto brands alone, the top proximal brands are Lucas Oil, KTM USA, Yamaha Motor, Arctic Cat, Two Brothers Racing, Phoenix Pro Scooters, Auto Alliance, Valvoline USA, Lance Camper, and Castrol. Some are related to off-road motocross riding, while others are not. For example, Lucas Oil, Valvoline USA, and Castrol are global automotive oil brands.In contrast, the top ten proximal brands to FMF Racing emerging from using all categories of data are KTM USA, Polaris Snowmobiles, Fox Racing, Mickey Thompson Performance Tires & Wheels, Two Brothers Racing, King Shocks, Arctic Cat, Addictive Desert Designs, NISMO, Skunk2 Racing, and MBRP performance exhaust. Upon further investigation, we find that they are all related to off-road motocross riding. These results indicate that our approach with engagement data from brands across industries can learn better brand representation and thus reveal a highly precise market structure. External Validity and Comparison with Other Approaches Market structure identified based on consumer surveyTo assess the external validity of our approach, we conduct a survey on Amazon Mechanical Turk (MTurk), which is a reliable source for data collection and marketing analytics ([43]). Prior market structure literature has also administered brand perception survey on MTurk ([ 7]). Following this prior study, we surveyed 28 automobile brands (after ignoring the other 150 brands that are related to motorcycle or automobile accessory such as tires, parts, and oil). Specifically, we recruited 500 MTurk participants, each of whom was required to be in the United States and have a good MTurk record (successful completion of at least 100 assignments with a minimum 95% rate of approval). Each participant was asked to rate the similarity between a focal automobile brand and the other 27 automobile brands on a scale of one to five. To avoid fatigue due to information overload, each participant was randomly assigned to work on one task. Participants were also asked to indicate their age, gender, and whether they owned an automobile. Details of the participants' demographics information and the survey design are presented in Web Appendix WA6.In the survey, participants could choose ""N/A"" if they were not aware of the automobile brands. Brand recognition rate was 88.2%, implying that 11.8% of ratings were not applicable due to lack of brand awareness. We aggregated the survey data and built a 28 × 28 matrix, where each cell represented the pairwise brand similarity, and denoted it as the ""survey matrix."" We also used the brand representations learned from our approach to construct another 28 × 28 matrix of brand similarity, which we denoted as the ""deep learning–based matrix."" The correlation between two matrices is significantly positive (r = .385, p = .000). This result provides additional evidence on the validity of our deep learning–based approach for market structure identification. We also did an additional check where we calculated the correlation between the survey response and that constructed by our approach but using only automobile (within-industry) data. The correlation is.152 (p < .05), which is not as substantial and significant as the correlation between the survey response and our approach using all industry data. We present the market structure learned from the survey data in Web Appendix WA7 as an external validity. Market structure identified based on Google TrendsTo provide further external validity of our approach, we use Google Trends data to identify market structure and examine how it aligns with our approach of using online social media users' brand engagement. Google Trends provides an interest score for every search query across regions and languages, as measured by an aggregated search volume over time. A higher interest score means that queries are more popular in a specific region and time. Google Trends data have been widely used by industry ([44]) and academia ([ 6]; [ 9]; [22]; [48]) to address marketing and economic problems (e.g., competitive analysis). Researchers have also shown that this score is consistent with consumers' purchase interest in general ([ 6]; [ 9]).To determine relative popularity for every pair of brands, we make a search query consisting of two brand names—for example, ""Toyota BMW"" for the brands Toyota and BMW. For every brand pair, we can obtain an interest score returned by Google. For example, in the United States in 2017, the interest score is 13 and 85 for the query ""Toyota BMW"" and ""Toyota Honda,"" respectively. This indicates that consumers in general are more interested in searching Toyota and Honda together, compared with searching Toyota and BMW together. Validation on airline industryIn the first validation exercise, we focus on the airline industry and the derived market structure. We have 19 airline brands in our data set, including U.S. domestic airlines and international airlines (Figure 5, Panel C). For every brand pair, we first obtain a Google search interest score in the U.S. region in 2017 (the same as our engagement data period). Then, following previous work ([35]), we calculate the similarity between two brands A and B as sim(A,B)=interest(A,B)∑b∈Sinterest(b,B) , where S is the set of all brands (e.g., 19 here). [35] use the co-occurrence of two brands in an online discussion forum instead of a Google search interest score. We also calculate similarity for every pair of 19 airline brands using 300-dimensional vectors derived from our deep network representation learning on the engagement data using cosine similarity.To check whether the two aforementioned similarity scores are similar to each other, we calculate their Pearson's two-tailed correlation between two sets of 361 (= 19 × 19) similarity scores. It is significantly and highly correlated ( r=.630,p=.0000 ). This indicates that our social engagement-based market structure is similar to that derived from Google Trends. Because prior studies have shown that the Google search data have a high correlation with a consumer's actual purchase interest ([ 6]; [ 9]), we can conclude that users' social engagement with brands also contains valuable information for deriving brand relationships. Validation on travel industryIn the second validation exercise, we focus on the travel industry, including not only major travel brands but also many small and local travel brands (see the ""Large Brand Versus Small Brand"" subsection). There are 241 travel brands in the data set. Similar to the first validation exercise, for every brand pair, we obtain a Google search interest score in the United States in 2017 (the same as our engagement data period). Among the 241 travel brands, Google Trends does not return scores for 90 brands (i.e., showing ""your search doesn't have enough data to show here""), which results in data for 151 remaining brands. Although individual brands show a considerable amount of search, only four brand pairs return nonempty interest scores.[ 9] This data sparsity may be attributed to the uniqueness of the travel category. Many of the travel brands are local/small businesses, such as the travel agencies ""Spirit of Boston"" and ""Historic Philadelphia."" Naturally, they do not receive as many queries as large brands. Moreover, consumers may search travel agency brands in different queries, but they very rarely search two travel brands in the same query. Therefore, there is not enough data for Google to aggregate and return the cosearch score. This analysis highlights the limitation of the cosearch-based approach, which is likely to suffer from the data sparsity issue. In contrast, our approach built on large-scale brand–user social engagement data can provide valuable marketing insights not only for large international brands but also for small local brands. Practical Actionability How to compare market structure maps?In a practical setting, marketing managers may need to quantitatively determine the quality of derived market structure maps, based on which they can infer actionable insights. We evaluate the conceptual maps using a standard metric—silhouette score ([ 1])—which has been adopted in prior market structure literature ([12]). The silhouette coefficient is calculated using the mean intracluster distance (a) and the mean nearest-cluster distance (b) for each sample, as b−amax(a,b) . The values of silhouette score range between −1 and 1 (1 being the best and −1 the worst). Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster. Recall that our approach can naturally group brands that have similar representations in the high-dimensional space. An ideal market structure would favor brands that are concentrated and exhibit clean cluster structures. We conduct K-means clustering on the brand representations and compute the mean silhouette coefficient of all samples.In the ""Within-Industry Market Structure Analysis"" subsection, we qualitatively show that our approach—without prespecifying product-market—reveals more interesting and coherent brand insight than using brand engagement data within only one industry. Next, we vary the number of clusters in K-means and calculate the silhouette coefficient of different methods. The result in Figure 8 shows that our approach using all brand engagement data consistently achieves better clustering than using only the automobile brand engagement data. For example, when we cluster 168 automobile brands into two clusters (i.e., K = 2 in K-means), our approach achieves a silhouette coefficient of.334, while the approach using only the automobile engagement data has a low silhouette coefficient of.043. The silhouette coefficient of our approach gradually converges to.10 as the number of clusters increases. In contrast, the approach using only automobile industry data stays near.01, indicating a poor separation among automobile brands. This analysis not only confirms the superiority of our approach without prespecifying product-market boundary but also enables marketing managers to determine the quality of derived market structure maps. How much brand–user engagement data are needed to derive a good market structure?We have shown that our approach can derive good market structure with large-scale social engagement data. In practice, it is easy to obtain a relatively comprehensive set of brands across different categories and associated user engagement from social media marketing platforms, such as Socialbakers. However, a marketing manager may not have enough resources to collect as large a data set as we have, raising the question as to whether our approach is sensitive to the size of data for obtaining a good market structure. To answer this question, we calculate the correlation between the similarity of pairwise brands generated using the full data set and that generated by a fraction of data selected at random. Figure 9 presents this result, showing that the correlation reaches over.90 when 40% data is used (and.7 when 12.5% data is used), and it starts to converge to the market structure generated by using the full data. This analysis suggests that our approach is relatively robust to the amount of engagement data used. Marketing managers can use this analysis as guidance to determine the amount of data resources needed. In addition, we examine how the number of prespecified industries affects the robustness of market structure maps and present the analysis in Web Appendix WA8.Graph: Figure 8. The clustering silhouette coefficient of 168 automobile brands.Graph: Figure 9. Pearson correlation between the similarity of pairwise brands generated using a percentage of full data and the full data. Evaluation using link predictionIn studying market structure, there is a lack of ground truth about the identified structure, that is, an understanding of what the ""true"" structure is, which makes demonstrating the performance of various proposed methods challenging. We introduce an alternative approach, adopted from network analysis literature ([27]), to evaluate the identified market structure. An identified market structure is a function of the brand representation, and so an accurate representation is more likely to identify valid market structures. This approach is supported by prior research showing a strong relationship between brand image and the characteristics of a brand's supporters and followers ([ 7]; [25]; [34]). If a network learning method were capable of accurately representing network nodes accounting for these relationships between brands and users, then it would be able to predict the future links between brands and users accurately. Therefore, we use a cross-validation procedure under a link prediction research design, where we predict the most likely newly formed links of user–brand engagement in an out-of-sample network given the brand vectors and user vectors learned from a training network. This research design is widely used in the network analysis community to evaluate network clustering algorithm performance ([27]; [51]). In our context, we use the user–brand interactions from the first half of the time span in our data to build a training network (G0,1) and use the second half to build a testing network (G1,2). The likelihood of a link formation is measured by the proximity of a learned brand vector and a learned user vector. Note that link prediction performance is significantly correlated with the quality of learned vectors, given the assumption that a better network representation learning can predict new interactions between users and brands with a high degree of accuracy. We provide details of the link prediction experiments in Web Appendices WA2–WA4. Overall, our analysis shows that ( 1) link prediction using representation learned from our brand–user network performs better than a reduced brand–brand network (a widely used method in extant approaches), ( 2) deep learning–based methods learn better representation than shallow machine learning methods, and ( 3) our deep learning–based model is robust and able to handle sparse networks as compared with baselines. Case Studies on Market Structure DynamicsMarket structure evolves over time and can change dramatically, especially under an unexpected industry shock. Whether our proposed method can be adaptively learned is also of interest as it could provide useful insights to marketing practitioners. In this section, we analyze how market structure changes under exogenous shocks by analyzing two case studies: Amazon acquiring Whole Foods and Tesla introducing the Model 3. We take a before-and-after strategy where we use data for the three months pre- and postevent announcement day and calculate the change in distance from the focal brand (i.e., Amazon and Tesla) to other representative brands selected from the same category. The purpose of the event study is to examine how a focal brand relationship with other brands changes as a major event occurs. Specifically, for Amazon–Whole Foods, we select several brands from the retail and e-commerce category, and for Tesla, we select several brands from the automobile category. We calculate the change between focal brand i 's representation wib and target brand j 's representation wjb before and after the specific event using cosine similarity: cossim(wiafterb,wjafterb)−cossim(wibeforeb,wjbeforeb) . Therefore, positive numbers indicate a similarity increase, whereas negative numbers indicate a decrease in similarity. Amazon Acquires Whole FoodsAmazon acquired Whole Foods in June 2017. This acquisition has had a significant impact on the grocery and retail industries. At the time, it was widely believed that Amazon planned to use its acquisition of Whole Foods to enter the online grocery delivery business. Amazon and Whole Foods ran separate Facebook pages. After the merger of the two firms, we see from Figure 10 that Amazon is more proximal to retail brands as measured by cosine similarity, while the proximity to other relevant brands decreases slightly. For example, the cosine similarity between Amazon and Lowe's Home Improvement decreases by.184. In contrast, the cosine similarity between Amazon and other super-market retailer brands increases. Among them, proximity of Amazon to Whole Foods increases by.202, and between Amazon and Kroger by.165. As inferred from our data-driven model, Amazon even becomes more proximal to Walmart, indicating that Amazon's competitive market structure landscape has shifted. By further examining our data, we find that, after the Whole Foods acquisition, the number of common users who interact with both Amazon and Whole Foods on their public Facebook pages increases. Some Amazon users posted comments on Whole Foods' fan page mentioning Amazon. For example, in the Whole Foods post, ""Here are 6 New Healthy Products Coming to Whole Foods in March,"" a user who had liked an Amazon post earlier commented, ""You mean AMAZON... as they bought Whole Foods...right?"" This direct link between Amazon and Whole Foods leads the deep autoencoder to increase the proximity between the two brands. Moreover, in another Whole Foods post, a user who had liked a Kroger post earlier posted, ""The quality has gone downhill and prices have soared.... You've made Kroger look appealing...."" Although we do not find that this user has ever interacted with Amazon's Facebook page before, her interaction with Whole Foods leaves an implicit connection between Amazon and Kroger, which can be captured by the deep autoencoder. In short, after Amazon acquired Whole Foods, online social media users who are Amazon's fans pay more attention to Whole Foods, and users who are fans of other supermarket brands engage more with Whole Foods due to the acquisition event. As a result, the deep autoencoder captures the dynamics and updates the brand representation accordingly.Graph: Figure 10. Similarity change of Amazon to other brands in retail and e-commerce industry.The acquisition by Amazon has an impact on the market structure of Whole Foods as well. In Figure 11, we consider Whole Foods as the focal brand and calculate the change in proximities to other brands before and after the acquisition. Drawing on the results, we observe that Whole Foods' proximity to other retail brands such as Target, Walmart, and Best Buy increases. Among them, the proximity to Amazon increases the most due to the increase in the number of common users between them. In contrast, Whole Foods' proximity to supermarket brands such as Goya Foods, Enjoy Life Foods, and HelloFresh decreases slightly. Second, the magnitude of change in proximity values is smaller than those of Amazon to other brands. This seems to indicate that the acquisition has had less impact on Whole Foods, as it is still positioned around other supermarket brands, while Amazon is expanding closer to the grocery retail category.Graph: Figure 11. Similarity change of Whole Foods to other brands in retail and e-commerce industry.Although this analysis is retrospective, it highlights that our approach offers managers a series of multiple snapshots of the structure over time to measure a brand's relative position change, thus identifying potential market structure change. Suppose a supermarket chain brand A observes that Amazon is moving closer to A's position on the map. This may indicate that Amazon is getting more engagements (likes or comments) from A's customers. Given that one motivation of liking a brand's Facebook post is to receive some benefit from the brand (e.g., coupon, discount), it could further indicate that Amazon is conducting effective promotional marketing campaigns on social media. No matter the underlying reasons, the increasing proximity of Amazon on the brand map can at least provide an early warning to A's marketing managers to the potential threat. Late response to the competition may harm the brand and eventually the whole business.Next, we validate the case study of Amazon's Whole Foods acquisition using Google Trends data. Similar to the first external validity exercise, we choose 29 ""retail"" brands (including Walmart, Target, Macy's, Best Buy, Walgreens, Lowe's, Whole Foods, IKEA, Sears, 7-Eleven, Dollar General, Sam's Club, Dollar Tree, CVS Pharmacy, Aldi, Barnes & Noble, Costco, Kroger, Meijer, Safeway, Office Depot, Rite Aid, Albertsons, ShopRite, and The Fresh Market) plus Amazon and obtain their interest scores for every brand pair in the United States in 2017. Note that we exclude some small retail brands such as Goya Foods because their Google cosearch interest scores with other brands are mostly 0, indicating insufficient search data for the brand.The Pearson's two-tailed correlation between two sets of 900 (= 30 × 30) similarity scores is significantly high before ( r=.675,p=.0000 ) and after ( r=.758,p=.0000 ) acquisition. This result confirms the external validity of our social engagement–based method. We observe that for Amazon, before the Whole Foods acquisition, the most similar brands were Barnes & Noble, Macy's, and Best Buy. After the acquisition, the most similar brands are Whole Foods, Barnes & Noble, and Macy's. For Whole Foods, before the acquisition the most similar brands were The Fresh Market, Albertsons, and ShopRite. After the acquisition, the most similar brands are The Fresh Market, Amazon, and Safeway.We obtain further search interest data for one year after the acquisition (June 2017 to June 2018) to examine whether the market structure change is sustained for a long period after the acquisition announcement. For Amazon, the most similar brands are still Whole Foods, Barnes & Noble, and Macy's. Other grocery ""retail"" brands such as Kroger and The Fresh Market become more similar to Amazon than before the acquisition. For Whole Foods, the most similar brands are The Fresh Market, Safeway, ShopRite, and Amazon. Because Whole Foods is still Amazon's most similar brand among these retailer brands, this indicates that for Amazon, the acquisition impact holds for the extended period of analysis. It seems that the acquisition has less of an impact on Whole Foods, as Whole Foods is still positioned around other supermarket brands. All findings are consistent with our case study using social engagement data, which provides external validity to our results. Tesla Announces the Model 3Tesla sells two types of sedans: the Model S and the Model 3. The Model S is a luxury premium sedan with a larger range of acceleration and customization options, while the Model 3 is designed as a more affordable mass-market electric vehicle. The Model S can cost over $100,000 depending on the configuration, while the Model 3 costs approximately $35,000. After the announcement of the new Model 3, we see that Tesla becomes more distant from luxury car brands and moves closer to nonluxury car brands. We can see in Figure 12 that the cosine similarity between Tesla and the luxury car brand Maserati decreases by.209. Similar trends exist between Tesla and other high-end or luxury car brands such as BMW, Mercedes-Benz, Audi, and so on. Meanwhile, Tesla becomes more proximal to Kia, Mazda, and other more affordable car brands.[10]Graph: Figure 12. Similarity change of Tesla to other selected brands in the auto industry. Testing for SignificanceIn the previous analysis, we compute the distance change between the focal brand (i.e., Amazon or Whole Foods) and other brands before and after the acquisition. We can see that there is a significant increase in similarity between Amazon and Whole Foods after the acquisition. However, whether this distance change is caused by the acquisition or other unobserved factors, such as the difference of data split and/or noise, still remains unclear. Therefore, we conduct further analysis by randomly splitting all data before the acquisition into two parts (i.e., d1 and d2, with d1 before d2). We then measure the distance between Amazon and Whole Foods using d1 and d2 separately. We repeat this process 30 times using different data cuts in the preacquisition data. The average distances between the two brands across using all d1s and d2s are.228 and.232, respectively. The two-tailed t-test on the distance is.055, which indicates that there is no statistically significant difference between the distances between Amazon and Whole Foods before the acquisition in different cuts of the preacquisition data. Accordingly, the substantial increase in similarity between Amazon and Whole Foods is not attributed to sample differences.We perform a similar process on Tesla's introduction of the Model 3. In particular, we choose one nonluxury brand, Mazda, and compute its distances to Tesla before the event using various data splits. The average distances between Mazda and Tesla across using all d1s and d2s are.185 and.191, respectively, with a p-value of.076. This seems to indicate there is no statistically significant difference between Mazda and Tesla when the cutting point of data varies before the event. Therefore, we conclude that after Model 3's announcement, Tesla becomes more similar to nonluxury automobile brands on the social media platform. Note that we also conduct analyses on Tesla and other automobile brands, and the results are consistent. Implications and ConclusionOur proposed approach examines millions of user engagements with thousands of brands and focuses on the early stage of the customer journey. This allows for visualization of potentially overlapping product-market boundaries across many categories and helps managers identify latent threats and potential opportunities, which cannot be done with extant methods that focus on later stages of the customer journey (lower levels of the purchase funnel) within categories. As an example, for Southwest, is Airfarewatchdog a potential competitor that might draw visitors away, or is it a complementor that would increase visits to Southwest? Having identified the overlapping market with Airfarewatchdog, Southwest could invest more attention to evaluate the exact nature of this relationship. If Airfarewatchdog is a competitor, then Southwest might focus on developing strategies to differentiate itself and channel visitors to its website exclusively. If it is a complementor, then Southwest might run display ad campaigns on Airfarewatchdog's website. In addition, both Disney Cruise and Hyatt are closely associated with Southwest, with common users who like these brands on social media; therefore, Southwest could run mutually beneficial joint and cross-promotions with these other brands. In fact, all these brands could join in a dynamic coalition loyalty ecosystem built around a fluid partnership of products, services, and experiences, thereby providing a unifying customer value proposition that could be difficult to compete against ([ 4]). Identifying such unusual or unforeseen insights is the greatest advantage of our approach.Another important strategic use of our market structure maps is to identify competitors and complementors across industries and track how these relationships change over time. While [15] apply text analysis to 10-K statements to identify such grouping based on product descriptions that the firms provide, we provide a more dynamic structure based on actual customer/user social media activities. Moreover, our market structure map is more forward looking and predictive of emerging competition and complementors and more proactive than those based on 10-K statements, which can be viewed as reactive. Because [14] show that merging firms with more similar product descriptions in their 10-Ks results in more successful outcomes, using our market structure maps to identify merger-and-acquisitions targets (firms sharing common users) may have similar benefits. We leave this for future research.The power of our method lies in its ability to capture the dynamic changes in market structure. Because the maps are based on the analysis of big data that can be collected in a relatively short window of time, our methodology can track changes in their relative position when firms introduce new products, new promotions, and new marketing initiatives. The case studies that we highlighted provide good illustrations of this. In addition, although we have not analyzed this in the article, firms can deploy our method to enhance their social network-based marketing efforts by better targeting specific potential customers, because user nodes in the network are also learned and represented as vectors in the same multidimensional space as brands. Our link prediction design demonstrates a possible use for targeting. Finally, our proposed method is generalizable to other similar platforms if we can construct a brand–user network from public fan pages' engagement data. We implemented our proposed method using NVIDIA P100 graphics processing unit, with Tensorflow as the back-end deep learning framework. For future research to replicate or practitioners to adopt, we have provided details regarding data collection, data cleaning, and deep model architecture, and model the fine-tuning process in Web Appendix WA1.While marketing analytics techniques have extensively used consumer personal data to derive valuable insights, they raise many privacy and ethical concerns. How to balance these two important aspects has become a key consideration for many marketing scholars ([ 3]; [52]). Our approach provides a useful example. The only input to our network representation learning method is the brand–user network, which can be publicly obtained from brands' social media page.Our research has some limitations. Given the nature of our data, our method cannot examine stockkeeping unit–level competition as is done by some of the extant methodologies using lower funnel data. From this perspective, we recommend our methodology as a complement to extant methods and for higher-level brand strategies and tactics. Future work could examine how perceptual maps vary by customer segment using lower-funnel data such as purchase frequency and purchase amount. Second, our analysis is conducted on one social network, Facebook. Even though Facebook is one of the largest online social networks, with billions of users and thousands of brands, it is likely that users on different platforms exhibit different engagement behavior, and some of the research findings may not be generalized to other platforms. For example, it is reported that Instagram users and Facebook users fall into different age groups (Pew Research Center 2021). We could apply the same technique to other social media platforms and compare findings. Finally, each link in the user–brand network is created when the user engages with the brand on the public page. Facebook has introduced various reaction emotions to the platform to allow users interact with brands in different ways, such as ""Like,"" ""Love,"" ""Care,"" ""Haha,"" ""Wow,"" ""Sad,"" and ""Angry."" Future work could build a multirelational network to deeply capture brand–user engagement heterogeneity. " 32,Increasing Organ Donor Registrations with Behavioral Interventions: A Field Experiment," Although prior research has advanced our understanding of the drivers of organ donation attitudes and intentions, little is known about how to increase actual registrations within explicit consent systems. Some empirical evidence suggests that costly, labor-intensive educational programs and mass-media campaigns might increase registrations; however, they are neither scalable nor economical solutions. To address these limitations, the authors conducted a field experiment (N = 3,330) in Ontario, Canada, testing the effectiveness of behaviorally informed promotion interventions as well as process improvements. They find that intercepting customers with materials targeting information and altruistic motives at the right time, along with streamlining customer service, significantly increased registrations. Specifically, the best-performing intervention, prompting perspective taking through reciprocal altruism (""If you needed a transplant would you have one?""), significantly increased new registration rates from 4.1% in the control condition to 7.4%. The authors followed up with seven posttests (total N = 3,376) to find support for their theoretical predictions and to explore the mechanisms through which the interventions may have operated. This article provides evidence for low-cost, scalable marketing solutions that increase organ donor registrations in a prompted choice context and has important implications for public policy and societal welfare.","Current statistics on organ donation point to an ever-increasing demand yet inadequate supply of available donors. For example, in Canada, more than 4,400 people are waiting to receive lifesaving organ transplants ([15]). Similarly, in the United States, there are over 113,000 individuals currently on the transplant waiting list, and 22 people die each day waiting ([23]). Concerningly, the gap between those needing transplants and those receiving them continues to widen ([23]). One way to address the growing demand for transplantable organs is to increase the number of individuals who register as donors ([16]). To illustrate, in the United States, the ""conversion"" rate for registered donors who have died and are medically suitable for organ donation is nearly 100% ([72]).Low registration rates are especially prevalent in countries with explicit consent registration policies—that is, individuals must opt in to become organ donors—compared with countries with presumed consent policies—where individuals are organ donors by default but can opt out ([34]). Although changing the default appears to be a promising intervention ([66]), the impact on actual donations has been mixed due to, among other things, uncertainties about a deceased person's donation preferences ([21]; [48]). Furthermore, changing registration policies involves implementation challenges and ethical considerations surrounding informed consent ([27]). To date, most jurisdictions have maintained their existing policies ([58]), thus prompting the following question: What can be done within explicit consent systems to improve organ donor registration rates?Prior research has identified factors predicting organ donation attitudes and intentions, such as having adequate information about organ donation as well as altruistic motives (for reviews, see [25]] and [53]]). However, attitudes and intentions do not consistently translate into actual registrations ([54]). In Canada, where we conducted our study, even though the vast majority of Canadians (90%) are in favor of organ donation, and 81% say they themselves would be willing to register ([33]), only 23% have actually registered their decision to become an organ donor ([15]).Furthermore, the limited work focusing on registrations has largely tested elaborate and costly interventions outside of organ donor registration systems (e.g., testing workplace education programs and mass-media campaigns; for reviews, see [26]] and [30]]). Finally, in a recent article on living organ donation, [14] emphasized that promotional messages, despite being the primary focus of most charitable giving research (e.g., [24]; [40]; [41]; [55]; [83]), are only one aspect of the marketing mix that can be employed to solicit donations. Through a qualitative study, they outlined how the entire marketing mix—product, price, promotion, place, process, and people—may be employed to reduce experiences of sacrifice in the complex and cumbersome process to encourage living organ donations.Our article contributes to the limited empirical evidence for low-cost and scalable marketing solutions to increase actual in-person organ donor registrations in current explicit consent systems. In addition, this research contributes to our understanding of how to employ multiple elements of the marketing mix to help achieve the objectives of nonprofit organizations ([14]). Specifically, our field experiment demonstrates how intercepting customers with promotional materials at the right time (an information brochure and perspective-taking prompts), along with other process improvements (streamlined customer service that includes additional time to review the promotional materials and a simplified form), can increase new organ donor registrations. By leveraging behavioral science to design our marketing interventions, we contribute to the understanding of how to reduce the intention–action gap in the context of organ donation, improve public policy, and enhance societal welfare. Theoretical Foundations Organ Donation SystemsOrgan donation systems typically take one of two forms: explicit consent and presumed consent. In explicit consent systems, individuals have to enroll in the organ donor registry (i.e., opt in). The specific process can vary, but it usually occurs when people obtain or renew identification (e.g., driver's license) at a local government office such as the Department of Motor Vehicles. Although many countries have recently made online registries available, to date the majority of registrations still take place offline ([22]). For example, in Ontario, where our research was conducted, 85% of registrations in 2016 occurred in person at ServiceOntario centers (i.e., Department of Motor Vehicles equivalent; [75]).Within explicit consent systems, one technique used to ""nudge those who are willing donors into becoming registered donors"" is mandating or ""prompting"" choice ([72], p. 125). In prompted choice contexts, customer service agents ask individuals whether they would like to register their consent to be a donor. Prompting forces individuals to decide, instead of waiting for them to actively volunteer their consent unsolicited, which can help overcome procrastination, inertia, and limited attention ([72]). However, even when prompting is implemented, many jurisdictions continue to have low organ donor registration rates ([22]; [36]). For example, at the time of our field experiment, only 24% of the 12 million eligible Ontarians were registered, despite using prompted choice ([74]).With the rise of behavioral science and nudging in policy, one solution that has received attention is changing legislation from explicit consent to presumed consent, where individuals are considered organ donors by default but can opt out ([27]). Recent evidence finds that donation rates are approximately 30% higher, on average, in countries with presumed consent systems ([66]), though default policies were argued to be only one factor among many that determined donation rates (e.g., systems for obtaining family consent, transplant infrastructure, religious beliefs). In fact, some countries even observed a decrease in donations when moving to presumed consent ([ 5]; [21]). To date, very few countries have chosen to change their default policy to presumed consent (e.g., Singapore, the United Kingdom, Argentina, the Netherlands; [58]), as doing so can present several challenges. These include ( 1) a significant investment of time and money ([48]), ( 2) ethical concerns relating to informed consent and individual autonomy ([42]; [43]), and ( 3) ambiguity for the surviving family about the deceased's wishes ([12]). Together, these factors lead [72], p. 121) to conclude,We favor the policy of prompted choice because there is no evidence that a viable alternative system would save more lives (and hence is superior in terms of the interests of Patients), and because we think that it does the best job of respecting the rights and interests of Potential Donors and Families. At the same time, we favor more nudges, and better choice architecture, to improve the prompting. Current Research on Organ Donation BehaviorTo date, research focused on actual organ donor registrations remains rare. [30] recently conducted a narrative review of all empirical research measuring actual registrations. Although they identified 24 studies, the authors concluded that many suffered from methodological weakness including selection bias, confounds, and self-reported dependent variables. As a result, the authors could not conduct a meta-analysis or provide clear prescriptions for how to improve registrations. In fact, only eight studies were found to be methodologically robust, and even among these, the majority were conducted outside the current registration systems and tested interventions that were relatively complex, costly, and labor intensive. For example, interventions tested included town halls with expert panels ([ 2]), mass-media campaigns ([61]), and workplace lunch-and-learn programs with presentations by transplant recipients and donor family members ([52]). Though some interventions proved promising (e.g., educational programs), they were neither scalable nor economical ways to improve registration rates within existing explicit consent systems.One notable exception is recent work by [60], who conducted a field experiment testing the effects of adding persuasive messaging (e.g., using reciprocal altruism or social norms) to an online prompt to join the national organ donor registry in the United Kingdom. The authors found that their reciprocal altruism message (""If you needed an organ transplant would you have one? If so please help others."") performed best and increased individuals' sign-ups from 2.3% in the control condition to 3.1%. This study was the first to illustrate the potential for low-cost, scalable interventions, in general, and persuasive messages, specifically, to improve actual organ donor registrations. However, it was unable to distinguish between new and existing donors. In addition, it was conducted outside of the typical organ donor registration system. For example, it was conducted online at a time when most transactions were done in person ([81]), after drivers completed their government transactions. Given both the novelty and practical importance of these findings, there are several opportunities to extend this research that are worth pursuing. For instance, what might this effect look like for in-person transactions and on only new registrations? Would these findings replicate when applied within the more typical explicit consent registration system?Following an early release of these findings ([10]), [49] wanted to test the effectiveness of reciprocal altruism persuasive messages on registration intentions in both online and in-person contexts. The authors found that reciprocal altruism primes significantly increased intentions to register online but had no such effect in person. Moreover, no significant effects were found on proxies for donation behavior (i.e., whether participants accessed optional information on organ donation), regardless of mode of delivery. Although this study did not measure actual registrations, it provides some support for the use of reciprocal altruism messages in the organ donation context, while also calling into question whether such messages would be effective for in-person registrations.Taken together, although prior research on organ donation suggests that targeting altruistic motives and information may be promising, we know little about how to encourage actual, new, in-person organ donor registrations, especially in a low-cost and easy-to-scale manner. We designed our field experiment to explore these opportunities. Promotional Materials to Increase Actual Organ Donor RegistrationsPromotional materials are commonly used by for-profit and nonprofit organizations to inform, persuade, and motivate actions. For example, nonprofit organizations can employ promotional tools when individuals are in the deliberation stage of a donation to help them proceed to the actual decision stage ([14]). We built on prior research to develop and test promotional materials to increase new organ donor registrations by providing information (with an information brochure) and enhancing altruistic motives (with perspective-taking prompts). We supported our interventions with improvements to streamline the registration process (i.e., additional time to review the promotional materials, intercepting customers at the time of decision, and a simplified form). InformationMost theories of human judgment and decision making argue that individuals make decisions on the basis of declarative knowledge—facts and information—that comes to mind at the time of decision making (for reviews, see [32]], [84]], and [85]]). The information can be obtained from external sources (such as an information brochure) or retrieved internally from long-term memory. Information can also increase procedural knowledge—the knowledge of how to perform a specific task ([59]). Studies have shown that providing information targeting each of these types of knowledge encourages action (e.g., [51]; [57]; [70]; [78]). In the context of organ donation, declarative information has been shown to be effective at enhancing attitudes, especially when framed positively (e.g., ""one individual organ donor can donate organs [e.g., heart, lungs, kidneys, liver] to eight other people""; [56]). Messages providing procedural information about how to become a donor were particularly effective at enhancing attitudes when individuals were unaware of these details ([44]). Building on the aforementioned research, we predict that providing individuals with promotional material (i.e., an information brochure containing declarative and procedural information), specifically at the point when they are deciding, will make that information salient to them and increase new organ donor registrations. Altruistic MotivesAltruistic motives arise from empathy toward others and have been found to drive prosocial behavior across multiple domains ([ 6]; [ 7]; [ 9]; [79];). Research finds that one effective way to evoke altruistic motives is through perspective taking, considering a situation from a different point of view ([82]). In the context of organ donation specifically, perspective taking correlates with positive attitudes and willingness to register ([18]; [46]), though we do not yet know if perspective taking can be employed to reliably increase actual registrations. Moreover, perspective-taking manipulations have been tested almost exclusively in the lab until recently, and therefore, it is unclear how their effects would translate to field settings (cf. [37]; [60]).We sought to test the effectiveness of enhancing altruistic motives on organ donor registrations in the field with three differing perspective-taking prompts: imagine other (IO), imagine self (IS), and reciprocal altruism (RA). The imagine other prompt asks individuals to consider how others would feel in a situation, enhancing one's pure altruistic motives to help ([ 7]). Alternatively, asking individuals to imagine oneself in the situation—imagine self—can increase both one's altruistic as well as self-interested motives ([17]), which some have suggested may be even more effective for encouraging prosocial behavior ([ 9]; e.g., [ 3]; [29]). Perspective-taking prompts can also encourage prosocial behaviors by making additional psychological concepts salient (e.g., reciprocity). For example, recent research has found that the reciprocal altruism prompt ""If you needed an organ transplant would you have one? If so please help others"" significantly increased online organ donor registrations ([60]). Such a statement evokes both self-interest and reciprocity by pointing out that that if individuals are willing to accept an organ, they should also donate ([39]; [67]; [77]). Given that prior research has found that benefits to the self as well as benefits to others can drive prosocial behaviors ([ 7]; [13]), we predicted that prompting these three types of perspective taking (imagine other, imagine self, and reciprocal altruism), would increase actual organ donor registration rates. Field Experiment Methods ParticipantsOur field experiment was conducted over a 2.5-week period (March–April 2014) in one preselected ServiceOntario location in Ontario, Canada—an explicit consent jurisdiction with prompted choice. To maximize the generalizability of our findings, we carefully considered the choice of location for the experiment. The specific location chosen has a sizable population (one of the largest and busiest centers in the province) that is demographically representative of Ontario's total population on several preselected characteristics including age, income, education, and religion.[ 7]In 2014, when our experiment was conducted, all Ontarians were required to visit ServiceOntario centers in person for almost any public service including driver's license, health card, and photo identification transactions, thus reducing sampling bias concerns.[ 8] Each individual who visited this service center was a participant in our experiment (N = 3,330), and all participants visiting on a given day were exposed to the same experimental condition or phase. Because the timing of the phases and conditions was defined before the start of the experiment, neither the center, nor individual service agents, nor the researchers had control over which individuals received each condition. On average, 214 individuals visited the center each day. New donor registrations were measured using the service center's computer system. For each individual, we observed the type of transaction(s) they completed, the service agent they saw, and whether they registered during that visit as a new organ donor (yes/no). No identifying information about the participants and service agents was shared with the researchers to maintain privacy and protection of all parties involved. Standard Organ Donation Registration Process and MaterialsThe standard in-person registration process in Ontario is similar to that of many prompted choice jurisdictions. Upon arrival at ServiceOntario, individuals are given a number at the reception desk and wait until their number is called. Once called on, individuals perform the transaction(s) they came for at a service agent's counter, and during these transactions they are prompted to register. That is, they are asked by the service agent if they would like to register their consent as an organ donor today. Only if they affirm, they are then given the center's standard organ donor registration form to complete on the spot (Figure W1–1 in the Web Appendix).The standard organ donor registration form is a black-and-white full-page document consisting of three sections (for a visual of the standard form, see Figure 1). The left-hand column primarily presents legal and procedural information about organ donation (e.g., ""You have the right to decide whether or not to consent to the donation of your organs and tissue""). Although this information is meant to inform consumers, it is handed to consumers only after they agree to register and thus comes too late in the process. On the right-hand column of the document, the top portion serves to collect personal information from the individual (e.g., name, address, date of birth). It is important to note that for in-person registrations, that information is redundant as customers just completed another transaction in which they provided that information (e.g., renewing a driver's license) and therefore unnecessarily lengthens the process. Finally, on the bottom right-hand side, individuals are asked to indicate their consent and sign the form.Graph: Figure 1. Registration form layouts. Experimental ConditionsWe created five experimental conditions for our field experiment: A control condition that involved two process changes (time and simplification) and four promotion intervention conditions (information brochure and three perspective-taking prompts). The process changes made in the control condition were also present in all of the experimental conditions, enabling us to test for improvements specifically resulting from our promotion interventions. For an overview of the persuasive materials tested, see Table 1; for the forms and brochure, see Web Appendix W1.GraphTable 1. Overview of Promotional Materials. ControlOur control condition included two process changes designed to streamline customer service and support our interventions tested in the experimental conditions. First, individuals were handed the organ donor registration form with their waiting number when they arrived at the reception desk to allow adequate time to read, process, and consider the materials (vs. during their transaction[s] at the agent's counter). An additional benefit of handing the form out in advance is that it reduces the variation in registrations that may be caused by the individual service agents[ 9] and ensures that every customer is handed a registration form.Second, to increase the salience of our interventions, we created a simplified version of the organ donor registration form[10] (for a comparison of the forms, see Figure 1; for the simplified form, see Figure W1–2 in the Web Appendix). In addition, behavioral research consistently finds that reducing the effort required to perform an action, or even just simplifying content (i.e., reducing ""sludge""; [69]; [71]), can increase the number of people taking action ([11]; [68]). In creating our simplified version, we first removed all material from the standard form that was not required, legally or practically, for in-person transactions. As a result, the simplified form retained only the consent questions and a place to sign the form, focusing individuals on the decision at hand. In addition, we added a colored banner on top to add visual appeal, which, importantly, provides a location to highlight the perspective-taking prompts in three of the experimental conditions. Finally, we printed this smaller form on a half sheet of cardstock paper because the thicker, sturdy paper would enable individuals the option to complete the form without counter space (e.g., while waiting).In all experimental conditions, participants received this simplified form upon arrival at the reception desk. These two process changes—extra time and simplified form—ensured that we could better capture the effect of our key behavioral interventions. Stated differently, without this streamlined customer service, our interventions might not be able to improve registration rates, because they may be overlooked and/or would come too late (i.e., after responding to the prompt to register). The control condition serves as a clean, conservative benchmark that enables us to quantify the effect of our interventions. InformationBehavioral researchers have argued that policy interventions are more likely to be successful if you consider their timing and prompt people when they are most likely to be receptive ([11]). In our information condition (info), we aimed to intercept customers and provide information at the right point in time. Although this condition presents promotional material, it is primarily a test of improving process; Ontario's standard organ donation brochure (see Figure W1–3 in the Web Appendix) includes detailed declarative (e.g., ""1 organ donor can save 8 lives"") and procedural information (e.g., ""Registering is easy. Ask at the counter or do it online."") presented in a visually appealing and easy-to-process way (i.e., cleanly organized and relatively large font). This brochure is always readily available to take from self-serve brochure stands in the waiting area of ServiceOntario centers. It is also mailed to individuals with their driver's license renewal notice. However, in this condition, we tested the impact of handing out the brochure along with the simplified form to all customers when they arrived at the reception desk. By providing this information while individuals were waiting and deciding, we predicted that this would increase their likelihood of reading the brochure; the salience of the information during decision making ([84]); and, in turn, registrations. Perspective-taking promptsThe other three experimental conditions targeted altruistic motives using perspective-taking prompts. Participants were handed the simplified registration form with one prompt printed in the colored box at the top of the form (Table 1). First, our reciprocal altruism prompt, ""If you needed a transplant, would you have one?"" (adapted from [60]]), leveraged self-interest, empathy, and reciprocity. Our second prompt, imagine self, leveraged self-interest and empathy by stressing that without enough registered donors, they (the reader) or their loved ones might not have a transplant available if needed. Finally, our third prompt, imagine other, leveraged empathy by highlighting that without enough registered donors, others might not have a transplant available if needed. We conducted a pretest to confirm that each prompt induced the correct perspective taking as intended (see Web Appendix W2). Timeline of Field ExperimentOur experimental conditions were each run consecutively for three business days. In addition, we added two phases, each pre- and postexperiment, in which we ( 1) measured registrations with the standard registration process (standard process phases) and ( 2) included time for acclimating service agents to the procedural changes caused by the experiment and informing them registrations would be tracked, a jurisdictional requirement (acclimation phases; for more details, see Web Appendix W3). During the acclimation phases, visitors were given our new simplified form but otherwise the service center followed the standard registration process. That is, the registration form was handed to individuals during their transactions at the service agent's counter only if they agreed to the registration prompt. Therefore, in total, data collection spanned an eight-week period beginning on February 24, 2014. For an overview, see Table 2.GraphTable 2. Timeline of Field Experiment. 20022242921990070 Notes: The chosen location operates six days a week, Monday through Saturday. Due to required messaging sent to service agents informing them it was ""the last week"" of the organ pilot, A2 was run for one business week (six days) instead of three days. Also, although the standard process phases were planned for two weeks each, one day during SP2 landed on a holiday and the service center was closed. Therefore, we have data for only 11 instead of 12 days. ResultsTo analyze the impact of the field experiment on new organ donor registrations, we start by presenting model-free evidence. Here, we adopt a-two-part approach. First, to test the effectiveness of our behavioral interventions, we compare the registration rates of each experimental condition with that in our control condition. Second, to explore the impact of our field experiment process changes (additional time and simplified form) relative to the standard registration process, as well as the impact of acclimating service agents to the experiment, we compare the registration rates in the pre- and postexperimental phases with that in the control. We then present logistic regressions that control for time-varying factors, such as the day of the week and the available agents. Next, we present a set of validity checks and robustness checks that test our hypotheses using alternative modeling specifications. Finally, we present a summary of seven follow-up posttests to provide support for our theoretical predictions and to explore the potential mechanisms through which our interventions may have operated.[11] Model-Free Evidence Behavioral interventions versus controlThe gray-shaded bars in Figure 2 illustrate how registrations were affected by our interventions (see also Table W1–1 in the Web Appendix). A joint F-test confirmed that there were statistically significant differences between the conditions (F = 5.285, p <.001). New organ donor registrations were highest in the reciprocal altruism condition (7.4%; 95% confidence interval [CI] = ±2.08%). In fact, reciprocal altruism was the only condition that significantly increased donor registrations (Δ = 3.30%, p =.012, Cohen's h =.143) relative to our control condition (4.10%; 95% CI = ±1.51%). That said, registration rates in the reciprocal altruism condition were not significantly greater than any of our other interventions at the 5% level (info: Δ = 1.51%, p =.279; IS: Δ = 2.36%, p =.076; IO: Δ = 2.40%, p =.070).Graph: Figure 2. New organ donor registration rates, raw means.Notes: White bars represent pre-post experimental phases, and gray bars represent our experimental conditions. Conditions are presented in order of implementation. Error bars represent ±1 SE. Pre- and postexperiment phases versus controlWe found that the registration rate in our control condition was not significantly different from that in any of the pre- and postexperimental phases (all ps >.18). Logit Model Behavioral interventions versus controlBecause each experimental condition was run for three consecutive days, we account for potential differences across days when treatments were applied. To do so, we ran a fixed-effects logistic regression using all individuals who engaged in a transaction during our entire eight-week data collection period (i.e., during the experimental and pre- and postexperimental phases; N = 10,043). In this analysis, we controlled for day of week fixed effects and agent serving each individual (""agent"") fixed effects.[12] Our experimental control condition served as the baseline. To be conservative, we use robust standard errors and have clustered all standard errors at the daily level as this was the unit where treatments were assigned ([ 1]). The dependent variable was whether an individual registered as a new organ donor (see Table 3). Results are presented in terms of odds ratios (ORs), that is, the odds that an individual registered as a new organ donor given a particular treatment (e.g., information condition) compared with the odds of the individual registering in the control condition.GraphTable 3. Organ Donor Registration Results (ORs) and Robustness Checks. 30022242921990070 *p <.05.40022242921990070 **p <.01.50022242921990070 ***p <.001.60022242921990070 Notes: Column M is our main specification, column R1 adds a linear time trend, column R2 adds agent × day-of-week fixed effects, and column R3 adds customers per agent. Standard errors are robust and clustered at the daily level. Our dependent variable is registration as a new organ donor (i.e., consent = 1, no consent = 0).The results of this analysis appear in column 1 of Table 3 (overall model: χ2 (d.f. = 39) = 1,978.19, p <.001). As with our model-free results, we found that being exposed to the reciprocal altruism prompt significantly increased the odds of registering compared with the control condition (OR = 1.84, p <.001). After controlling for day of week and agent effects, we found two additional conditions to be significant. Compared with the control, the odds of registering were also significantly higher in the information condition (OR = 1.99, p <.001) and imagine self condition (OR = 1.81, p =.015). Follow-up pairwise Wald comparison tests under our main specification (i.e., agent and day-of-week fixed effects) show that these three interventions did not significantly differ from one another in their effectiveness (all ps >.699; for details, see Table W1–2 in the Web Appendix, columns ""Info"" and ""RA""). Pre- and postexperiment phases versus controlAlthough we did not observe a significant difference in the model-free results, after adding agent and day of week fixed effects, we find that registrations were significantly higher in the preexperiment acclimation phase (OR = 2.09, p =.002) compared with control. All other comparisons with our control condition remained nonsignificant.During the preexperiment acclimation phase, attention was being drawn to organ donor registrations, service agents were exposed to our new form for the first time, and service agents were informed that organ donor registration rates were being tracked during this pilot period. Critically, for the interpretation of our subsequent experimental conditions, registration rates declined immediately afterward. Nevertheless, to test whether these specific process changes impacted registration rates, the following section presents a validity check that decomposes the different elements of our interventions. Moreover, we subsequently test additional controls to account for changes in agent's behavior with a series of robustness checks. Validity Check: Decomposing the Elements of our InterventionsGiven the constraints of our government partners, we were limited in the number of experimental conditions we could run. As a result, we were unable to counterbalance and test each element of our interventions individually. It was for this reason that we created an experimental control condition: to serve as a clean benchmark against which we compared our interventions. However, a result of this approach is that compared with the center's standard process, each of our interventions comprised a combination of multiple changes. For example, unlike the standard process, those in the information condition received additional time, a simplified form, and an information brochure (see Table 2). Our analysis formally separates the following seven elements: ( 1) the standard process, ( 2) the simplified form with agents aware of tracking, ( 3) additional time, ( 4) information brochure, ( 5) reciprocal altruism prompt, ( 6) imagine self prompt, and ( 7) imagine other prompt. We calculated the effect of each these elements using a fixed-effects logit model predicting the odds of registering (see Table 4).GraphTable 4. Analysis of Organ Donor Registrations (Odds Ratios): Validity Check Decomposing the Elements of the Interventions. 10022242921990070 *p <.05.200022242921990080 **p <.01.300022242921990100 ***p <.001.400022242921990100 Notes: Our dependent variable is registration as organ donor (i.e., consent = 1, no consent = 0). SP = preexperiment standard process phase, RA = reciprocal altruism condition, IS = imagine self condition, IO = imagine other condition.This analysis illustrates that the process changes initiated during the preexperiment acclimation phase (i.e., using the simplified form and making service agents aware of the fact that we were tracking registration rates) by themselves did not significantly increase an individual's odds of registering in comparison to the standard process, nor did handing the materials out in advance to provide more time. However, as predicted, and in line with our previous results, we find that adding a reciprocal altruism prompt did significantly increase individuals' odds of registering (OR = 1.870, p <.001). Similarly, providing an information brochure significantly increased individuals' odds of registering (OR = 1.697, p =.012). Finally, the results presented in Table 4 show that changing from the reciprocal altruism prompt to the imagine self prompt had no significant impact on the odds of registering (OR =.833, p =.479), nor did changing from the imagine self to the imagine other prompt (OR =.814, p =.456). Robustness ChecksIn this subsection, we perform a series of robustness checks to confirm that our results are robust to alternative sets of controls, in particular a time trend, agent–day of week interactions, and the number of customers per agent (for the inclusion of type of transaction controls such as health card or driver's license, see Web Appendix Table W1–4; for the use of different ""baseline"" conditions, see Web Appendix Table W1–5). Time trendWe incorporated a linear time trend in column R1 of Table 3 to address a potential confound related to history and ensure our results were not being driven by seasonality or a change in agents' behavior due to the experiment ([63]). Our results are robust to the inclusion of a time trend, and if anything, increased the estimated effect of the information and reciprocal altruism interventions. The estimated effect of imagine self also increased, but the effect was not significant due to a higher standard error. Agent-day of week interactionIn column R2 of Table 3, we incorporated agent–day of week fixed effects to account for the possibility that certain agents may perform better on certain days of the week. Again, our results are robust to the addition of this control. Customers per agentAnother potential confound is how busy the center was. Customers may have been more likely to register on days with longer wait times, as they would have more time to attend to the materials. Conversely, agents may be more likely to promote organ donation transactions when wait times are short. To account for this, we controlled for the ratio of the number of customers to the number of agents working on a given day in column R3 of Table 3. Our results remain robust to the addition of this control. Follow-Up Laboratory and Crowdsourcing Platform PosttestsTo provide support for our hypotheses and test possible alternative mechanisms, we conducted seven online posttests (five preregistered) with 3,376 North American participants.[13] The purpose of these experiments was to explore the process through which our interventions may have been operating. In all of the posttests, participants were randomly presented the actual materials of one of our experimental conditions (between-participants design), they were asked a series of questions aimed at measuring their reactions toward these stimuli, and our experimental control condition served as the point of comparison. To increase confidence in significant findings, we sought to replicate them across posttests. Next, we discuss the main results from our posttests (for an overview of measures and findings see Appendix W4). Posttest 1: risk perceptions (N = 502, Amazon Mechanical Turk [MTurk] workers)Posttest 1 examined whether our interventions may have affected perceptions of risk. It is possible that our materials focused participants on specific aspects of risk, such as the risk of not having a transplant available if needed, leading to self-interested motivations to act ([17]). Conversely, our interventions may have shifted participants' focus away from the risks and onto the benefits of registering ([80]). We assessed the perceived risk of five factors: ( 1) needing a transplant in the future, ( 2) being able to get a transplant if needed, ( 3) encountering an organ donation shortage, ( 4) the medical system treating organ donors fairly, and ( 5) the medical system allocating organ donations efficiently. We found no evidence that our interventions significantly changed risk perceptions compared with control (all ps >.44). Posttest 2: reciprocal altruism prompt mechanism (overall N = 767)In posttest 2a (N = 403, MTurkers) and 2b (N = 364, Ontario students), we examined an important assumption of our reciprocal altruism prompt: that individuals envision themselves in a position where they are accepting help, which then increases their likelihood of reciprocating (see [77]]). Specifically, we hypothesized that the presence versus absence of the prompt ""If you needed a transplant, would you have one?"" would significantly increase registration intentions, both when participants were explicitly asked to answer the prompt or—as in our field experiment—when they were simply presented the prompt. However, individuals' registration intentions—that is, their hypothetical registration likelihood (seven-point scale), consent decision (indicating ""I would consent to help save lives by becoming an organ and tissue donor""), and exclusions decision (indicating ""I would wish to donate any needed organs and tissue"")—were not significantly different from the control at the 5% level (all ps ≥.091). In hindsight, these null effects are likely the result of the aforementioned organ donation intention–action gap that many countries, including Canada, observe. In support of this, we found extremely high registration likelihood ratings and consent rates even without the reciprocal altruism prompt (posttest 2a: Mcontrol = 5.55 and Mcontrol = 99.0%, respectively; posttest 2b: Mcontrol = 5.57 and Mcontrol = 96.6%, respectively). Finally, as a manipulation check, we explored whether people envisioned themselves accepting a transplant, if they needed one. Indeed, when forced to respond to the reciprocal altruism prompt, almost all participants answered with ""yes"" (posttest 2a: 92 of 99, posttest 2b: 87 of 88). Posttests 3–7 (N = 2,107, MTurkers)Posttests 3–7 aimed to examine a combination of ( 1) perceptions of the materials (e.g., educational, thought-provoking, focused on self vs. others); ( 2) the thoughts and feelings evoked from our interventions, including positive and negative emotions, feelings of sympathy, comfort registering; and ( 3) the extent to which our interventions affected participants' general views on organ donation (i.e., the importance and norms of registering). Finally, we conducted an internal meta-analysis ([45]) focusing on each of the measures assessed in at least two posttests. Next, we present the reliable insights from this meta-analysis.First, in terms of perceptions of our promotional materials, we found that indeed the brochure was viewed as significantly more educational (β =.84, 95% CI = ±.36; p <.001). In addition, it was seen as more emotionally positive (β =.67, 95% CI = ±.23; p <.001) and less emotionally negative (β = −.37, 95% CI ±.27; p =.008). These results are in line with our hypothesis that the information condition (brochure) would make additional information salient.Second, in terms of thoughts and feelings, we found that our perspective-taking prompts evoked significantly stronger feelings of sympathy (in fact, all of our interventions, including the information condition, did so; βinfo =.63, 95% CI = ±.37; βRA =.52, 95% CI = ±.37; βIS =.60, 95% CI = ±.37; βIO =.76, 95% CI = ±.37; all ps ≤.006). In addition, all but the reciprocal altruism intervention (p =.17) were viewed to be significantly more focused on others (βinfo =.44, 95% CI = ±.27; βIS =.53, 95% CI = ±.27; βIO =.55, 95% CI = ±.27; all ps ≤.003; no difference for focus on self: all ps ≥.10). These results support that our perspective-taking prompts in the field experiment likely evoked stronger altruistic motives.In terms of alternative mechanisms, it is possible that our interventions may have stimulated new considerations ([84]), impacting registrations in our field experiment. For example, in our posttest meta-analysis we found that all of our interventions, except the imagine self prompt (p =.113), were rated as significantly more thought-provoking (βinfo =.45, 95% CI = ±.30; βRA =.34, 95% CI = ±.30; βIO =.43, 95% CI = ±.30; all ps ≤.027). It is also possible that providing information caused individuals to feel more knowledgeable ([31]), feel greater comfort with the decision to register ([50]), and feel more prepared to register ([78]), positively affecting registration rates. However, in our meta-analysis, we found no evidence that our interventions influenced feeling knowledgeable (all ps ≥.407), comfort registering (all ps ≥.260), or preparedness (all ps ≥.518). Finally, our interventions could have changed general perceptions of how important it is to donate ([ 6]) or how much it is the right thing to do (norms; [28]), in turn increasing registrations in our field experiment. However, we found no evidence for such effects in our meta-analysis (all ps ≥.300; for other nonsignificant measures that were explored, including feelings and perceptions of ethicality, see Table W4–3 in the Web Appendix). Summary of FindingsThe results of our field experiment support our prediction that marketing interventions grounded in behavioral science, targeting information and altruistic motives, would significantly increase new organ donor registrations in a prompted choice context. While our interventions did not significantly differ in effectiveness from one another in the majority our analyses,[14] our reciprocal altruism intervention (""If you needed a transplant, would you have one? If so, please help save lives and register today"") was the best performing. It led to the highest registration rates and was the only condition to significantly increase registrations compared with our control condition consistently across all analyses, including our model-free results. After including relevant controls (e.g., day-of-week and agent fixed effects), we found that our information and imagine self interventions also significantly increased registrations compared with control.Our posttests provide some initial evidence for the mechanisms driving our interventions. As we predicted, all of our perspective-taking prompts induced greater feelings of sympathy compared with control ([ 8]), and our information condition was rated as more educational. Moreover, all of our perspective-taking prompts, except reciprocal altruism, were perceived to focus more on others. The posttests also suggested some additional mechanisms through which our interventions may have been operating. For example, our brochure was found to be more emotionally positive and less emotionally negative than the control. Previous research has shown that declarative information is especially effective at increasing organ donation attitudes when framed positively ([56]), which may have contributed to its success. The brochure also increased feelings of sympathy and focus on others, suggesting that it may have targeted altruistic motives as well ([ 6]). All our interventions, aside from imagine self, were viewed to be more thought provoking than our control, suggesting that they may have changed what individuals were considering when deciding ([84]). Other mechanisms tested (e.g., risk perceptions, comfort registering, importance of donating, norms) were not supported. Although our posttests exposed participants to the actual materials used in our field experiment, it is critical to note that they were conducted outside the in-person, actual organ donor registration context (e.g., online MTurk and online university student samples) limiting our ability to draw conclusions about what occurred in our field experiment. Furthermore, previous research has shown that organ donation attitudes and intentions often do not translate into actual behavior ([47]). For these reasons, it is important to exercise caution when drawing conclusions from these posttests. General DiscussionThis field experiment contributes to the literature by testing marketing interventions to increase new organ donor registrations within the prevalent explicit consent systems. Prior organ donation research has primarily focused on factors that influence intentions and attitudes ([27]); however, the shortage of registered donors appears to be primarily a problem of inaction ([64]). To date, a small number of studies have documented some positive impact on actual registrations from elaborate education programs and mass-media campaigns ([30]), yet they provide little insight into how to increase new registrations within explicit consent systems in an economical and scalable way. This research contributes to the limited empirical evidence for low-cost and scalable marketing solutions, targeting knowledge and altruistic motives, to overcome the intention–action gap and improve registrations within the current systems. Specifically, we find that in our best-performing condition, prompting perspective taking through reciprocal altruism (""If you needed a transplant, would you have one? If so, please help save lives and register today"") significantly increased registration rates from 4.1% in the control condition to 7.4%, an 80% increase.Our work also contributes to our understanding of how to employ multiple elements of the marketing mix to help achieve the objectives of nonprofit organizations. Research on charitable giving has primarily focused on promotional strategies to solicit donations of time, money, and blood (e.g., [38]; [62]). Recently however, [14] qualitatively examined how the entire marketing mix could be employed, more broadly, to encourage living organ donations. We expand on this research by empirically testing interventions to support the more common request to register as a deceased organ donor. We demonstrate that intercepting customers with promotional materials at the right time, along with streamlined customer service—additional time and a simplified form—significantly increased new organ donor registrations. Importantly for practitioners, this streamlined organ donation process ensured that every customer was exposed to the materials and had ample time to consider them and complete the form. It also reduced the burden on the individual service agents to prompt registrations and reduced agent-caused variation in registrations. We obtained preliminary evidence, based on a small postexperiment survey, suggesting that our process changes may have reduced the time it took for individuals to register.[15] Processing transactions faster would save ServiceOntario time and money and could also lead to happier customers, as they would have shorter wait times. Moreover, other research has found that giving individuals something to read while waiting can make the time go by faster and increase satisfaction ([35]). Therefore, these simple changes (i.e., reducing ""sludge""; [71]) may help increase not only organ donor registrations but also the efficiency of the registration process as well as customer satisfaction.This work also expands our understanding of how altruistic motives can be leveraged to increase prosocial behavior. While prior research has largely tested perspective taking in the lab ([37]), we demonstrate its effectiveness in the field. In addition, we replicate and extend the generalizability of using reciprocal altruism to improve organ donation behavior ([60]) by demonstrating its effectiveness at encouraging new and in-person registrations within the typical registration system and in a different national culture. We predicted that the reciprocal altruism prompt would be especially effective due to its combined focus on benefits for self and others ([39]). While it did perform best, it is important to reiterate that our three perspective-taking prompts (reciprocal altruism, imagine self, and imagine other) did not significantly differ in effectiveness from each other across the majority of our analyses. The fact that all of our prompts improved registrations, albeit to varying degrees and not consistently significantly different from control, suggests that each of these different forms of perspective taking might be viable solutions for motivating organ donor registrations, though it would be important to test variations of their conceptualization in the field.Our results highlight the importance of carefully considering the process and timing of delivering promotional materials. For example, the fact that our information condition was successful (after controlling for day of the week and agent) is noteworthy because the brochure that we provided was the standard one that was both readily available in self-serve stands at ServiceOntario centers (throughout the experiment) and was mailed with all drivers' license renewal notices along with a standard registration form. Therefore, it is important for managers to intercept customers at the right point in time.For practitioners, our results highlight the critical importance of testing interventions in the field and measuring their realized impact. For one, individuals in our pretest (Web Appendix W2) did not accurately predict the relative effectiveness of our interventions. Moreover, it would have been reasonable to predict that merely providing individuals with a simplified form along with additional time to decide would increase donor registrations (i.e., our control condition; [11]), but our results revealed otherwise. Finally, in a recent meta-analysis, [19] suggested that nudge interventions tested in academic research tend to have a significantly larger effect than those tested ""at scale"" by government ""nudge units."" While our field experiment is similar to that of academic research in terms of sample size and effect size, many other features of our experiment suggest it is similar to research of nudge units: we tested elements of simplification, had relatively representative sampling, and our findings were released to the general public by our government partners, irrespective of the results. Together, these observations highlight the importance for researchers and policy makers to consider issues such as statistical power, selection effects, and characteristics of the interventions when planning at-scale implementations of interventions from academic research. Limitations and Opportunities for Future ResearchAlthough this experiment provides us with low-cost, scalable marketing solutions to increase actual organ donor registrations, we were unable to test the mechanisms driving our interventions and did not have the opportunity to replicate the study over a longer period or at a larger scale. For example, to maintain customers' privacy, we could not survey the individuals who came to the center or collect any personal information about them, and therefore, it was not feasible to administer manipulation checks or process measures. While our posttests provided some initial process evidence, they can only be considered as indicative, at most, because they were conducted outside the in-person registration context (e.g., online student samples, MTurk workers), and measured feelings, thoughts, and intentions, which do not always map onto behavior ([54]). Future research should systematically examine the underlying mechanisms driving interventions within field settings to facilitate generalizations.In addition, due to timing and design constraints we were unable to counterbalance each component of our interventions with a fully-crossed and randomized design. Although we estimated the unique impact of the elements of our interventions, future research could experimentally manipulate each to explore whether they had additive or interactive effects. For example, it may be worthwhile to formally test how information and perspective-taking work in combination with one another, as they may be more effective together than either condition alone.Finally, another area for future research would be to examine the role that customer service agents play in encouraging organ donor registrations. First, our data revealed significant effects of agents on registrations. Second, registrations in the preexperiment acclimation phase (a hybrid between the center's standard process and our subsequently introduced experimental process) were significantly higher than the preexperiment standard process phase and, after including day-of-week and agent fixed effects, also higher than our experimental control condition. In the preexperiment acclimation phase, service agents encountered our new simplified form for the first time and received formal communication from their manager drawing attention to the organ donation task and making them aware that registrations would be tracked. These factors may have led to excitement or changes in agents' behavior that in turn led to increased registrations. In all of our experimental conditions, we took care to limit any variation in registrations caused by the service agents by handing the form out in advance (i.e., when individuals arrived at the registration desk). However, these findings suggest that maximizing agent effectiveness could be another interesting avenue for future research aiming to increase organ donation rates. Conclusion and Policy RecommendationsWith thousands currently on the transplant waiting list, the need for organ donors is urgent, and as the population ages, the demand is only predicted to increase further. One way to address the ever-growing demand is to increase the number of individuals registered as donors within the prevalent explicit consent systems, in which low registration rates are especially common. In our field experiment we were able to increase new in-person registrations in a prompted choice context using easy-to-scale, low-cost[16] promotion interventions supported by process improvements. We were able to do so without imposing on individuals' freedom, raising ethical concerns (i.e., changing defaults), or passing new legislation. To illustrate the potential impact of our findings, if we were to assume that everything held constant over time and we introduced our best-performing intervention (reciprocal altruism) throughout Ontario, we could expect roughly 225,000 additional new registrations annually. While quantifying the effect of increased registrations on the ultimate goal—lives saved or enhanced—is challenging (see, e.g., [20]), research has shown that those who register their consent are significantly more likely to actually donate than those who have not ([16]). Specifically, the majority of registered individuals in Ontario who become eligible to donate are ultimately converted to donors ([73]), and [76] advertises that one single donor may save up to eight lives and enhance as many as 75 more lives.Compared with the center's standard registration process, all of our interventions significantly increased organ donor registration rates (see Table W1–1 and W1–2 in the Web Appendix). We recommended Ontario implement our reciprocal altruism intervention and track its performance for three reasons: ( 1) this intervention was successful in increasing registrations in both the United Kingdom ([60]) and our field study, ( 2) it significantly increased registrations compared with the control across all our analyses, and ( 3) it avoids the costs associated with printing additional brochures. In 2016, the Ontario government adopted our recommendation partially by introducing a somewhat simpler organ donor registration form with the reciprocal altruism prompt province-wide (compare Figure W1–7 with Figure W1–4 in the Web Appendix). For policy makers who want to use our insights to improve organ donor registrations in a similar context, we recommend implementing as many of the design elements of our marketing materials as possible (e.g., colored banner on cardstock), along with implementing the supporting process changes (e.g., simplified form, intercepting customers at the right time, providing time to attend to the materials). Together, we believe this research not only informs our understanding of how marketing can be leveraged to improve nonprofits' goals but also offers insights that could benefit society by increasing organ donor registrations. " 33,Informational Challenges in Omnichannel Marketing: Remedies and Future Research," Omnichannel marketing is often viewed as the panacea for one-to-one marketing, but this strategic path is mired with obstacles. This article investigates three challenges in realizing the full potential of omnichannel marketing: ( 1) data access and integration, ( 2) marketing attribution, and ( 3) consumer privacy protection. While these challenges predate omnichannel marketing, they are exacerbated in a digital omnichannel environment. This article argues that advances in machine learning and blockchain offer some promising solutions. In turn, these technologies present new challenges and opportunities for firms, which warrant further academic research. The authors identify both recent developments in practice and promising avenues for future research.","Despite the prevalence of new advertising and promotional channels and significant investments in data and technology, marketers are still struggling to generate and to prove sales results in an increasingly omnichannel world.—Eric Solomon, Senior Vice President, Nielsen ([62])Channels have traditionally been viewed as intermediaries that facilitate distribution and transfer of products from manufacturers to their customers.[ 3] Prior to the commercialization of the internet and subsequent digitization innovations, firms usually employed one type of channel such as a physical store, a call center, or a catalog. However, there were also instances where firms employed multiple channels to serve their customers. For example, firms such as L.L. Bean, Sears, and Lands' End sold their products in brick-and-mortar stores, in catalogs, and by phone. This practice gave birth to the idea of multichannel marketing. Subsequently, the idea of multichannel marketing moved beyond product fulfillment to include a whole gamut of interactions between a firm and its customers. [60], p. 96) define multichannel marketing as the ""design, deployment, coordination, and evaluation of the channels to enhance customer value through effective customer acquisition, retention, and development."" Therefore, in a multichannel context, although customers may interact with the firm across multiple channels before a conversion occurs, the firm's focus is on managing and optimizing the performance of each channel separately.The presence of multiple channels can alter how customers gather product information (e.g., [ 5]; [79]) and where they purchase these products ([64]). In addition, a portfolio of channels allows customers to self-select into their preferred channel at each stage of the purchase journey ([10]; [82]), thereby allowing the firm to access a larger base of customers. Furthermore, when an online retailer expands into offline channels, the firm may also see some benefits of complementarity (e.g., [ 8]; [49]). As a result, operating additional channels might result in customers increasing their purchases ([48]).With continuing growth in digitization, consumers today interact with firms across online, mobile, and offline media channels. This, in turn, has led to a shift toward ""omnichannel"" marketing, which emphasizes a unified consumer experience rather than just facilitating transactions. Furthermore, as [74] study indicates, the growing popularity of omnichannel marketing has been fueled by the idea that the different stages of the customer journey can be decoupled and delivered by various entities. In effect, for firms, omnichannel marketing entails managing a combination of different types of channels such that they align well with the way their customers search, purchase, and consume their products and share those experiences ([ 3]).[81], p. 176) define omnichannel as the ""synergetic management of the numerous available channels and customer touchpoints, in such a way that the customer experience across channels and the performance over channels is optimized."" In the ideal scenario, customers interact seamlessly with the firm across channels both internal and external to the firm, and the firm has full information on all customer touchpoints to provide a single unified experience across channels.However, this ideal faces several important hurdles in reality. As retailers adopt omnichannel marketing, it presents its own set of challenges and opportunities for the suppliers and other distribution channel partners. [ 3], p. 120) note that omnichannel marketing ""often encompasses not just the channels of distribution through which a supplier's products reach the consumer but also the channels of communication—owned, paid, and earned.""As we see it, this important observation made in [ 3] does not fit within the scope of [81] definition of omnichannel marketing. We broaden the scope of previous definitions and define omnichannel marketing as the synergistic management of all customer touchpoints and channels both internal and external to the firm to ensure that the customer experience across channels as well as firm-side marketing activity, including marketing-mix and marketing communication (owned, paid, and earned), is optimized for both firms and their customers. Thus, whereas [81] emphasize experience over transactions and [ 3] emphasize communications over sales, our view of omnichannel marketing considers sales, experience, and communications. Note that the synergistic management of touchpoints and experiences might affect important outcomes for firms, such as market share, profits, and customer lifetime value ([ 6]). The exact objective function is likely to vary across firms and its and customers' life cycle.Given its promise, it is not surprising that firms have invested heavily in omnichannel marketing. The transformation to omnichannel marketing has gained prominence in a wide range of industries, including consumer packaged goods such as Unilever, fashion retailers such as Bonobos, service providers such as Bank of America, restaurants such as Starbucks, and pharmacies such as Walgreens. However, firms also need to consider the cost of implementing customer integration (for details, see [20] and [34]). In the end, firms have to assess whether additional costs are commensurate with the expected benefits of undertaking omnichannel marketing. Our treatment of omnichannel marketing in this article focuses more on the customer side and the ensuing impact on revenues rather than on the supply-side costs that firms may incur in achieving such integration.Despite the promise of omnichannel marketing to manage how firms interact with their customers to drive growth, foster innovation, and improve long-term performance, we posit that this potential has not been fully realized. In our view, there are three main interrelated challenges that have prevented omnichannel marketing from realizing its full potential: Data Challenges: To fully realize the potential of omnichannel marketing, firms need information on all their interactions with each customer during the different stages of the customer journey. We include consideration of the communications between the firm and its customers, activities where the customers interact with the firm (or its partners) while gathering information, making a purchase, receiving the product, making a return, and receiving postpurchase service. Such data might not be readily available or easily usable. Marketing Attribution Challenges: For optimizing the customer experience across all channels, firms need to know the impact of various touchpoints on behavior and measure the return on investment of its marketing spend. In our opening quote from Eric Solomon, this is captured as ""prove sales results."" Such analysis may be challenging when the effect of a touchpoint can transcend multiple stages in the purchase funnel, when several occur concurrently, or when consumers go back and forth between different stages in their path-to-purchase journey. Customer Privacy Challenges: The promise of omnichannel marketing relies on using data on all the interactions between the firm and its customers. However, this can come at the cost of infringing on customer privacy. Therefore, an important challenge for a firm is determining how to embrace an omnichannel strategy while respecting consumers' privacy.Each section of this article elaborates on these challenges and discusses recent attempts to address them. We then propose promising avenues for future research in these areas. Challenge #1: DataFirms such as REI carefully plan for their customer experience to be unified across all touchpoints. While REI has a large physical footprint, it is mobile-centric and encourages its customers to use the app. For instance, if a customer clicks on a product in an email from REI and installs the mobile application, the app will note which nearest store has the product in stock. In addition, when customers visit a store, they are strongly encouraged to join the store Wi-Fi, log into the app, and check product availability. Disney and Bank of America are examples of other companies that have carefully integrated the customer experience across different channels ([27]). Data Challenges in Omnichannel MarketingOne of the main challenges that a firm might face in realizing the full potential of omnichannel marketing pertains to availability and usability of such data from various touchpoints. We can broadly classify such data-related challenges along two key dimensions: ( 1) gaining access to these data and ( 2) integrating these data from different sources. We elaborate on these points in the following subsections. The first column in Table 1 summarizes the key issues in each of these two dimensions.GraphTable 1. Data-Related Challenges, Remedies, and Future Research. Challenges in gaining access to dataAs noted previously, in omnichannel marketing, firms interact with their customers at multiple touchpoints, some within the firm and some beyond it. Within the firm, often, information on various contact points by the same customer resides in silos. As a result, a given unit might not even know what data are being collected by other units. For example, a firm's e-commerce platform team may not know what information about a customer exists in other divisions within the firm, and vice versa. Thus, the first bottleneck for effective omnichannel marketing is knowing what kind of data exist on the same customer within the firm ([87]). The extent to which a firm is siloed depends on how it approaches the role of data-driven marketing. In some organizations, the role is centralized within a large data science team. In others, the individuals are spread out among smaller units that might specialize in that area.Beyond the firm, the problem is compounded. For example, many of the touchpoints for a consumer interested in an automobile are not controlled by the manufacturer, which might use paid, owned, and earned media to engage with customers; provide product information; and possibly entice them to visit the distribution channel (i.e., its local dealership). Subsequent interactions such as test drives and price negotiations occur at these dealerships. However, neither the manufacturer nor the retailer has a complete view of the multiple interactions; worse, they may not even know whether such interactions occurred. Thus, even if a firm is efficient in cataloging what data exist on a customer in each silo of the firm, it may not know what data exist on the same customer beyond the firm.When a firm is aware of all the data that exist on a customer within (and even outside) the firm, the second challenge is the right to use it ([83]). One of the reasons for this bottleneck is that complicated administrative procedures can make data sharing between different departments with the same company very difficult, if not impossible. For example, in financial companies, one set of investments being made by customers may not be reported to other parts of the company. In addition, in industries such as health care and finance, regulations might impose restrictions on sharing of data across units. For example, [57] showcase the presence of data silos in the context of health care. They find that even within a hospital system, there is evidence of incomplete sharing of patient and clinical data. Integrating data from different sourcesEven if firms can surmount the challenges related to awareness of and access to data, managers still need to integrate the data to produce insights. There are two main problems that can arise with such integration. First, because each touchpoint with the customer may be managed by different entities (both within and outside the firm), they may be stored in different databases, using different rules, data formats, and reporting standards. As a result, it can be extremely challenging to match data on the same customer across different touchpoints ([61]; [73]).The second problem is that data from diverse sources may differ in terms of their reliability. For example, the sales department within a firm might have accurate information on the various interactions it had with the customer. However, the information on the other interactions assembled by the marketing department might be less accurate, perhaps because its data are more aggregated and/or acquired from third-party vendors with their own rules and market definitions that may not overlap completely with those used by the firm. Similarly, data on some interactions might be missing some key information, which could arise, for example, from a firm's internal infrastructural limitations. For instance, a firm's interactions with its customers' via its call center/customer support channel often requires manual entry of the details of customers' inquiries, which makes it prone to transcription errors. This is in contrast to sales transactions channels, where state-of-the-art point-of-sale information technology systems reliably automate the process of obtaining reliable data on customers' purchase history and product returns. Remedies to Address Data Challenges in Omnichannel Marketing Remedies to gaining access to dataAs noted previously, gaining access to data on different customer touchpoints can be difficult even if such data reside within the same organization. In such settings, is it possible to fuse customer data together without having to transport them across various departments within an organization?In the past few years, developments in artificial intelligence (AI) have addressed this problem. One such example is federated learning. Unlike standard machine-learning practice, in which the training data sit on one machine or in a data center, federated learning enables multiple parties to use data from multiple decentralized data servers to collaboratively construct a machine-learning model while keeping their respective servers' training data private ([45]). Over the course of several training iterations, the shared models get exposed to a significantly wider range of data than any single organization or department possesses in-house. Such an approach would be valuable in situations where regulations, such as those in the context of health care, preclude business units within a firm from sharing data.Additional challenges are introduced when moving from situations where data reside within a company to those where outside entities own some of the customer information. This warrants reconsidering the boundary of the firm. Firms can form strategic partnerships or engage in acquisitions to ensure access to data. There are two broad situations where such partnerships have proved to be fruitful. The first situation involves tracking known customers on the so-called third-party ""walled garden"" platforms (Google, Facebook, and Amazon). Platforms such as Facebook and Google now allow firms to import their own ""first-party"" data, such as lists of email addresses or phone numbers. This can help firms identify consumers with whom they have previously had contact. Similarly, e-commerce platforms such as Amazon's ""Amazon Publisher Services"" enable a firm to understand how its customers engage on Amazon across products. Another example of a successful data partnership is the acquisitions of large data brokers by the legacy media agencies. In particular, the acquisitions of Epsilon by Publicis and Acxiom by IPG are two prominent mergers and acquisitions that have the potential to enable highly personalized omnichannel customer experiences when data from the data brokers are combined with the vast scale and breadth of complementary agency services. That said, the recent decisions by Google and Apple to stop supporting open-source identifiers such as third-party cookies and the identifier for advertisers can erode some of the benefits from these remedies.The second situation pertains to tracking known customers and prospects across the open web. There have been some positive developments wherein third-party data providers enable retailers to track consumers' engagement with ad platforms such as Amazon, Apple, Facebook, Google, Verizon, and Walmart, among others. For instance, data brokers such as Experian, Acxiom, and LiveRamp have allowed firms to match information such as email addresses or cookies with other data sets, such as spending and demographic information. These examples point to the growing set of choices available for marketers and advertisers of all sizes to access and integrate customer data from different sources to successfully execute their omnichannel marketing campaigns.An additional challenge is that even if firms can access data from several sources, they may face instances where some of the information is missing. New advancements in AI and novel predictive algorithms offer promising avenues for addressing these challenges. For example, in online purchases, product returns are a serious threat to the profitability of manufacturers and retailers, especially in the case of experience goods such as clothing. [25] have recently developed a machine-learning-based approach to predict the probability that an item will be returned. In a similar vein, many companies are now monitoring the use of products and enhanced product fulfillment even before the customer shows a need. For instance, Amazon has patented ""anticipatory"" shipping to cut down delivery times by predicting what buyers are going to buy before they buy it. This trend of using predictive models to forecast customer behavior might enable AI-powered companies to ship products to consumers before they are ordered ([ 2]). While these algorithms have been developed to predict purchase and consumption behavior to curate products and content, they can also be used to identify missing pieces of information in the data. For example, if a firm observes purchase information, but not the consumption or product return information, the predictive power of such algorithms can be used to fill these data voids. Remedies to integrating data from different sourcesThere are two main ways that firms currently track consumers across devices and media that the firm controls. The first is deterministic tracking, which occurs when the firm can identify a consumer from multiple databases. For example, a subscriber of The New York Times would log in to both the website and the app using the same email login, allowing for perfect identification of the same user.However, it is common for firms to encounter situations where they cannot match customers across different databases. For example, a website that did not have a subscription model and did not require a login would not be able to easily track whether it was the same consumer visiting its desktop website, mobile website, or application. As cookie deletion becomes more prevalent, it will become increasingly difficult to track the same consumer returning to the website. Under such situations, probabilistic tracking is a promising approach to identify consumers as they browse across different devices. As the name suggests, probabilistic matching allows firms to use algorithms to probabilistically identify and track the same user across multiple touchpoints. Drawbridge, which was recently acquired by LinkedIn last year, is an example of a firm that uses probabilistic tracking. To implement probabilistic tracking, marketers have the option of deploying machine-learning models trained on user location data, triangulated from multiple devices. This would enable them to identify the best model for probabilistic matching.A novel set of technologies that have the potential to help track customer data and its integrity are blockchain technologies, such as those inspired by smart contracts and shared tamper-evident ledgers. Blockchain-based solutions offer a way to coordinate among different entities in the supply chain (e.g., different sources within a channel or even different channels per se).[ 4] A key feature of blockchain solutions to this challenge is an attempt to bring all the data into one protected location. If the standards are enforced when the data are entered, a well-designed blockchain system can provide data integrity as well. The data recorded in a blockchain may easily be made accessible to the participants.Blockchain technologies have been developed mostly in response to the success and popularity of Bitcoin, in which all transactions are stored in a blockchain. Bitcoin's novelty was in creating a reliable digital currency system without any need for a centralized trusted party who would protect against copying of digital assets ([37]). This is an example of a permissionless blockchain, as it operates without any gatekeepers—and thus, the number and identity of the participants is not known. A central feature of this type of blockchain is a shared ledger, which is reconciled among the participants via a consensus mechanism ([36]). In contrast, permissioned blockchains allow firms to control who can see their data and validate the transactions ([36]). From a firm's point of view, the key advantages of using a permissioned blockchain as opposed to a more regular means of storing data is that blockchain offers more data integrity, because by the nature of shared ledger, there cannot be discrepancy when two users see the same piece of data.Permissioned blockchains require some asymmetry in authority because there must be a trusted party or consortium to give permissions to access the system.[ 5] The level of involvement of the trusted party in maintaining the records would depend on the structure of the system. The trusted parties may be either a private company or a government agency. It is important to note that while permissionless blockchains can be slow and expensive, permissioned blockchains are much faster and cheaper. In the world of digital ads, Lucidity is such a player, constructing and running a permissioned blockchain and controlling access to it. It is a trusted party in a similar way that Google is a trusted party in running keyword auctions.Participants may be punished for ""misbehavior"" outside of the blockchain (e.g., with fines, access restrictions) and their permission to participate revoked. While there is still a need for a method to reach agreement between the participants, there is no need for such demanding consensus systems as with permissionless systems. However, it is important to emphasize that permissioned blockchains can also be viewed as a more efficiently run distributed database, rather than a distinctly different way of managing data. A distributed database is a database where multiple parties can make an entry (e.g., Google Docs, Dropbox). Here, the ""multiple parties"" are the parties representing different channels. From a firm's point of view, the key advantages of using a permissioned blockchain as opposed to a more regular means of storing data is that blockchain offers more data integrity, because by the nature of shared ledger, there cannot be discrepancy when two users see the same piece of data.There are several advantages for storing data and safeguarding their integrity that result from adopting a blockchain. Blockchain-based systems can help with standardization and unification of data, leading to better data integrity in digital supply chains, such as in the adtech and martech world ([30]; [33]). The current opaque and fragmented adtech supply chain does not allow for seamless cross-validation of ad campaign data from the different entities in the ecosystem that sit between the brand and the publisher, such as the demand-side platform, supply-side platform, ad exchanges and data management platform, that would ascertain the veracity of the data. One problem omnichannel advertisers often face is the reconciliation of a transaction in a given ad campaign when mapping it from a brand to a publisher—ensuring that the raw campaign data for a given transaction is the same across the different entities (e.g., the demand-side platform, ad exchange, and the supply-side platform) in the adtech supply chain ([33]). A blockchain-related solution could allow for proper ad engagement tracking that will lead to more precise digital attribution. Higher data quality achieved through transparency and unification of data streams from the different entities in the adtech ecosystem will allow firms not only to track delivered messages but also to set up smart contracts to automatically execute intricate programmatic advertising strategies and eliminate redundancy and irrelevance, to the benefit of both the advertiser and the customer. With data standardization and integration across different parts of the adtech supply chain, marketing messages in an omnichannel environment can be delivered consistently and data can be verified.[ 6]The adoption of blockchain-based data management systems can affect how customer data are combined and integrated in many other areas as well. Omnichannel marketers typically have a complex supply chain consisting of physical stores, home delivery, online browsing, and online commerce, all of which comprise a complex network of data points on different systems and in different entities. Despite the advances made, in today's world, retail agreements are largely manual and based on proprietary systems. To get integrated views of the inventory and the customer, this complex world of data and transactions needs to be merged. For example, if a retailer pilots a blockchain solution to trace the cotton being used for a line of T-shirts, its internal system needs to be able to communicate with its cotton suppliers' and contract manufacturers' systems with a high degree of automation and accuracy to enable full end-to-end supply chain visibility.In this context, blockchain-related systems offer several business benefits for retailers and their partners in the supply chain, both upstream and downstream, as they gather information from multiple channels in one system, inducing standardization and unification of data.[ 7] With transparent, real-time data access enabled by a shared database, retailers will know where their stock is at any point in time in that complex supply chain and where their customers interact with them at any touchpoint in that path to purchase. This real-time knowledge can lead to a faster, more transparent, and end-to-end integrated supply chain. Although the database is shared, it is not visible in its entirety by all players, thereby mitigating any privacy concerns.Finally, the smart contracting feature of blockchains—due to automated execution of agreements—can drastically reduce the transaction costs within supply chains, thereby potentially lowering the cost of goods sold.[ 8][39] highlight that blockchains could allow firms to use ""micropayments to motivate consumers to share personal information—directly, without going through an intermediary."" Such forms of micropayments could significantly negate the need for firms to pay third parties such as Google or Facebook to share customer information, as is currently undertaken by omnichannel firms. The extent to which this will enhance customer welfare will depend on the degree to which firms can use this information to provide the most relevant products or services for consumers. In summary, the increased integrity of the data resulting from standardization and unification through blockchain-related solutions also brings an indirect benefit by supplying both higher-quality data for advanced data analysis and predictive analytics about customers. Future Research Opportunities for Investigating Data Challenges in Omnichannel MarketingWhile many of the advancements discussed in the previous subsection have significantly improved firms' ability to acquire and utilize disparate data to have a unified view of a customer/prospect, they also present an interesting set of challenges and opportunities for future research. First, building on the work of [25], how can one decide which machine-learning methods may be best and are generalizable to impute missing pieces of information using data already available to the firm? One challenge with typical imputation algorithms is that they are context-specific. For instance, [16] model the incomplete information problem faced by credit card companies by using the interpurchase time distributions. While the model works well for a credit card application, its use may be limited for other applications where interpurchase times are less regular. Developing a more general approach that accommodates situations that do not occur periodically is a promising opportunity for future research.Second, to aggregate and manage data from different firms and/or units within a firm that track different customer touchpoints, it might be useful to have matchmakers who can deliver that function. Firms such as A.C. Nielsen have been successful in delivering this for a part of the customer journey. However, increasing the scope of such data collection efforts would require significant changes in how these data integration platforms are designed. In this regard, future research could discuss the optimal design of matchmakers/platforms that will collate information from different parties spanning different customer touchpoints.Third, what is the impact of data sharing within and across firms on consumers (e.g., prices they pay), firms (e.g., supply-chain efficiency, profit margins), and policy makers (e.g., market structure, efficiency, overall surplus)? [15] suggest that the answer might depend on the precision of customer-level information. They model two firms that each have their own set of loyal (price-insensitive) customers and are competing with prices for switchers. Each firm can classify its own loyal customers and switchers correctly with some probability (this is the imprecision in targeting). The key insight from their study is that while individual marketing is feasible but imprecise, improvements in targetability can be a win-win for competitors. The intuition behind this result is that when a firm becomes better at distinguishing its loyal customers from the switchers, it is motivated to charge a higher price to the former group. However, targeting is imperfect. Therefore, firms can make mistakes such as classifying price-sensitive switchers as price-insensitive loyal customers and charging them a higher price. These mistakes allow the competitor to acquire the mistargeted customers without lowering prices and, thus, reduce the rival firm's incentive to cut prices. Therefore, the study reveals that firms may be better off sharing information with their competitors. However, the kinds of incentives that will facilitate data sharing are still unclear. In this regard, it would be worthwhile to explore what kinds of mechanisms should be put in place to incentivize firms to share data with their up- and downstream partners as well as with their competitors.Fourth, if one were to deploy blockchains, how could one incentivize internal and external partners to participate in the blockchains? The existing commercial success stories typically rely on the strength of large players—for example, Walmart uses its bargaining power to force all its suppliers to use its blockchain. For such an incentive design problem, one needs to measure and quantify the economic benefits enabled by blockchain technology in interorganizational environments. These benefits include the decentralized management of digital assets, the algorithmic enforcement of agreements in the form of software programs, and the verification of data records in an adversarial environment. These benefits can incentivize internal and external partners to work collaboratively on the development and deployment of different blockchain-based solutions for their interorganizational environments. Certain applications of blockchain technology such as smart contracts could significantly influence the level of challenges and transaction costs between upstream and downstream partners within a supply chain. Smart contracts can also be adopted to reduce routine processes to a set of articulated conditions and facilitate frictionless execution. Research should consider whether these actions would mean that blockchain can have a measurable impact on transaction costs, firm boundaries, and interfirm governance.Fifth, a blockchain's decentralized consensus feature can eliminate information asymmetry as a barrier to entry and facilitate greater competition ([19]). Increased competition can, in turn, enhance welfare and consumer surplus. However, decentralized consensus affords greater information transparency, which, in turn, can foster tacit collusion. Tacit collusion can, in turn, result in higher prices and erode consumer surplus. Consequently, would blockchain-enabled omnichannel marketing efforts result in increasing or softening competition? Challenge #2: Marketing Attribution Attribution Challenges in Omnichannel MarketingUnlike multichannel marketing, where marketing investments are optimized on a channel-by-channel basis, in an omnichannel setting, such optimization needs to be done jointly across all distribution and communication channels ([89]). This becomes challenging when the purchase funnel has many stages and/or is traversed by customers in a nonsequential manner, as is often the case in the digital economy. That is, a customer might begin their search process in a brick-and-mortar store, form an initial consideration set, and then at some point in the near future restart their search process on a website leading up to a new consideration set and eventually make a purchase.Before omnichannel marketers can optimize their marketing efforts across various customer touchpoints, they need to understand the effectiveness and role of each touchpoint in the consumer decision journey and its incremental role on the overall sales conversion ([43]). Attribution is more complicated in an omnichannel setting because consumers self-select into different channels, and part of the difference in response to marketing interventions might be a result of such self-selection ([58]). As a result, inferring the causal effect of interventions, which is essential for attribution, might be difficult or probably even impossible. The potential number of communication paths is incredibly large, and there is no way to have sufficient causal variation. Not surprisingly, the Marketing Science Institute (MSI) has consistently highlighted attribution as the number-one priority in its research priorities since 2016.Attribution-related bottlenecks in omnichannel marketing stem from three key sources. First, a touchpoint in the customer journey might have an effect on multiple subsequent stages in the purchase funnel. Even if each marketing intervention can be uniquely linked to a transition from one stage in the purchase funnel to the next, it might not be appropriate to view the effect of the intervention as being restricted within the boundaries of a stage in the purchase funnel. For example, if search advertising resulted in a customer clicking on it and arriving at a firm's website, should it be given credit only for reaching the website or also for all subsequent on-site activities, including purchase, either in the same session or at a later point in time? There are two potential implications of this challenge. One implication pertains to the contract between the advertising platforms (and/or publishers) and the advertiser. The price that the advertiser is charged (and/or should be willing to pay) needs to reflect the downstream impact of the exposure. This issue is not specific to the context of omnichannel marketing. A second implication, which is more relevant in the context of omnichannel marketing, regards the appropriate allocation of resources across different touchpoints. For instance, the impact of a marketing intervention in one channel at an early stage in the purchase funnel might interact with the impact of another intervention in a different channel, possibly at a subsequent stage.Second, consumers may be interacting with the firm via multiple touchpoints simultaneously. For example, there is ample evidence that people frequently consume several media at the same time (see [22]; [50]; [51]; [77]). Multihoming in digital platforms is a well-documented phenomenon. In such settings, marketing efforts are likely to be concurrently directed at the consumer across different channels ([31]; [32]; [59]; [69]). Under such a scenario, the challenge is to apportion credit among different omnichannel marketing activities for a conversion. As noted previously, this requires firms to reconsider the design of contracts as well as the appropriate allocation of resources across different touchpoints.Third, many attribution methods are largely focused on quantifying which touchpoint gets credit when a purchase happens. However, if a purchase does not happen, which touchpoint(s) needs to be held accountable? The question of what is ineffective as a marketing touchpoint should take priority in a firm's marketing measurement approach, as that is an appropriate place to start the conversation around reallocation of marketing budgets from one channel to another. This can become more problematic if that touchpoint's failure to drive purchase also led other touchpoints to fail. For example, if a customer had a poor retail store experience, it might lead them subsequently to decide against buying products on the firm's mobile app; however, identifying that chain of causality can be challenging. A related problem arises when a firm uses only a subset of potential touchpoints. Under such a scenario, the effectiveness of unused touchpoints cannot be assessed. Together, these two scenarios highlight some key limitations of the traditional multitouch attribution (MTA) approaches.Fourth, another challenge with attribution is when the data belonging to different stages of the purchase funnel are aggregated at different levels. For example, television advertising investments may be available only at the market level, while search information may be available at the individual level ([42]; [46]). Therefore, although we can infer whether an individual customer was exposed to search advertising, we may not have equivalent information for television advertising. Consequently, we can potentially relate actions by individual customers to their search behavior, but not for television advertising. The first column in Table 2 summarizes the key issues related to each of these challenges.GraphTable 2. Attribution-Related Challenges, Remedies, and Future Research. Remedies to Address Attribution Challenges in Omnichannel MarketingHow should firms resolve the first attribution challenge—that the effect of a marketing intervention can carry over to subsequent stages? One way to address this problem is to employ extant methods that have focused on modeling long-term effects (e.g., [24]; [38]; [41]; [56]; [70]). While traditional attribution modeling has used aggregate metrics (e.g., overall TV ad budget, number of website visits, net social media sentiment), more recent research uses individual-level path-to-purchase data. This has enabled researchers to obtain a richer understanding of carryover and spillover effects across channels ([21]; [31]; [47]; [68]).[ 1] model customers' states in their decision processes using a hidden Markov model to assess the impact of various channels at different stages of the decision process. [ 4] propose a graph-based attribution model that maps the sequential nature of customer paths as first- and higher-order Markov walks and shows the idiosyncratic channel preferences (carryover) and interaction effects both within and across channel categories (spillover). [88] develop a hierarchical Bayesian model for individual differences in purchase propensity and marketing response across channels, finding that catalogs have a substantially longer-lasting purchase impact on customer purchase than emails.The second challenge pertains to the case in which firms might employ multiple touchpoints simultaneously (i.e., within each stage in the purchase funnel) and/or when consumers might be multihoming. In such settings, firms tend to use heuristics such as first touch and last touch to infer attribution. In recent years, several ""digital native"" companies have developed intricate ways to uncover and influence online consumer decision journeys and attribute the individual-level purchase conversion to the individual exposure to specific marketing messages. As a result, MTA has come into prominence in recent years ([48]). This body of research has demonstrated the limits of heuristics such as last- and first-click attribution shortcuts. For example, [23] find evidence that last-click attribution can underestimate the effectiveness of some types of interventions and lead to suboptimal budget allocation. In addition, research has explored mapping and visualizing different consumer journeys in the digital space across display and search ads ([31]), examining the impact of offline channel opening on consumers' online shopping behaviors or vice versa ([10]; [28]; [49]; [63]) and developing more efficient ways to analyze and store big data ([13]).However, MTA runs into problems when companies also use more traditional marketing communication channels such as TV, radio, print, and billboards, as even digital native companies such as Amazon and Kayak do. Individual-level exposure and response data are either not available for these channels or their collection is severely constrained by costs and/or privacy concerns.[ 9] Likewise, MTA typically does not account for nonpaid influences on individual consumers, such as online and offline word of mouth ([26]).Next, we consider the third issue related to attribution: understanding the effectiveness of unsuccessful and unexplored interventions. To this end, advertisers are increasingly undertaking carefully curated randomized field experiments and leveraging advanced machine learning and econometric methods to evaluate the effectiveness of marketing interventions. Methods such as multi-armed bandits ([67]) have the potential to address some of these challenges. Multi-armed bandit experimentation is good for situations where conditions can change over time. This is essentially an optimization-driven approach where the omnichannel marketer creates a series of ads, which can be delivered to users by running multiple concurrent combinatorial tests of the creative, and offers to find the combinations that deliver the best results (e.g., click, conversion, revenue) ([76]). Multi-armed bandit experimentation can be slower than traditional A/B testing, but it is more robust in dynamic contexts and thus has the potential to lead to a more reliable digital attribution analysis. Future Research Opportunities for Investigating Attribution in Omnichannel MarketingWhile these innovations in attribution modeling have significantly improved firms' ability to assign credit to a specific marketing touchpoint, several challenges remain, which serve as the basis for future research. First, attribution models still cannot link the transition across stages of the purchase funnel to a single marketing intervention. They typically presume that the impact of the previous intervention stops with the next step within the purchase funnel and that this impact does not carry over to subsequent steps within the funnel. This assumption is inconsistent, for example, with aggregate-level findings that content-related (vs. content-separated) ads generate site traffic that is more likely to convert in the add-to-cart and checkout stages ([23]). This attribution challenge can be addressed by assembling a rich data set that tracks customers across different stages of the purchase funnel and can link them to their various interactions with the firm at each of these stages. If such data have sufficient variation in terms of the extent of firm–customer interactions at different stages of the purchase funnel, we should be able to map the short- and long-term impacts of marketing interventions at different stages of the purchase funnel and beyond.Second, in many settings, omnichannel marketers may have access to customer-level data for some channels and only aggregate data for other channels. There is a well-established tradition in marketing that combines aggregate and disaggregate data ([11]; [12]; Chintagunta, Gopinath, and Venkataraman [17]; [18]; [66]; [75]). These studies have shown that the combination of customer-level and aggregate data (usually market-level sales data) allows for a better, much richer understanding of consumer heterogeneity than either micro or macro data alone. To the best of our knowledge, we are unaware of any attribution models that leverage aggregate and disaggregate data.Third, as omnichannel marketers adopt technologies such as blockchain, these firms will realize greater transparency and more reliable integration of consumer data across touchpoints within and outside the firm. Precise MTA modeling and empirical analyses require access to atomic user-level data, some of which come from touchpoints on assets owned by the firm (e.g., the data that the brand may own about a consumer surfing on its website or mobile app) and some from touchpoints on external sources (platform-owned data about a consumer created when that consumer interacts with the brand's ads on Google, Instagram, Amazon, and others). Examples of such granular information include details about the various touchpoints in the consumer path to purchase, the sequence of touchpoints, the kind of content published on a given touchpoint and time spent interacting with that content, the kind of ads (e.g., search, display, video) on a given touchpoint and the time spent interacting with ads, the time lag between different touchpoints, and how frequently the consumer visited that touchpoint in the past. Such fine-grained omnichannel data about consumer response to digital advertising eventually need to be verified, collated, and made accessible. In implementing marketing-mix and attribution models, it is important to verify the various customer touchpoints. Blockchain technologies can serve this purpose. This naturally warrants a better understanding of how the attribution effects change (in terms of both magnitude and reliability) with and without blockchain-enabled marketing platforms.Fourth, as discussed previously, one challenge relates to assessing the effectiveness of unexplored intervention options. Because marketers can potentially have a plethora of intervention options, exploring the effectiveness of each of these options presents a unique challenge. Approaches that balance the trade-off between exploration and exploitation (e.g., the multi-armed bandit approach) have proved to be promising ways to address this issue. However, their ability to scale to a large set of alternatives faced by a typical decision maker is unclear. Developing approaches that are scalable to interventions that are large in dimensionality might be a worthwhile avenue for future research.Fifth, the channels through which firms interact with their customers may differ in terms of the flexibility of contracts. For example, let us consider the communication touchpoints that a firm may employ to inform its customers about products. Historically, television advertising contracts are negotiated in advance and are largely irreversible ([86]). In contrast, keyword advertising can be changed instantaneously. Low flexibility limits how quickly a firm can experiment with the nature and volume of its interactions with customers, which is required for attribution. In instances where firms concurrently use multiple channels with varying levels of flexibility, can one exploit the differential flexibility as a new source of identification for attribution? Challenge #3: Privacy/IntrusivenessUntil recently, questions of privacy and questions of channel structure were far removed from each other. This is because, in general, channel management was associated with a lack of insight into customers' desires, purchases, and feedback. Lack of insight was very much bound up with the lack of data as firms had different experiences with different aspects of consumer behavior.However, in the omnichannel environment, which relies on a fully integrated view of the various customer touchpoints, privacy issues are becoming a crucial question in any discussion of channel management. The ability to use first-party data and match them with external activity on digital touchpoints not owned by the firms is both novel and attractive for firms, but such practices have been challenged by privacy activists ([80]). In particular, control of a customer's data that may give insight into future sales opportunities is something that, in theory, should be available to all channel participants due to the widespread nature of a customer's digital footprint. However, in practice, channel conflicts can arise when one channel partner claims ownership over these data and tries to exclude other channel partners. Such claims often rely on certain interpretations of privacy regulations and customer privacy preferences. As such, customer privacy concerns can often be in surprising conflict with channel coordination.There are several reasons why privacy will become an important factor in omnichannel marketing. First, the types of products sold via omnichannel marketing will expand. At the moment, many of the key examples of omnichannel marketing are products, such as coffee, that tend to have short customer decision journeys and for which customers are generally untroubled if their shopping habits are visible to others. Omnichannel marketing may ultimately be most useful, however, for high-involvement products that involve many stages of deliberation and research by the customer. Often, high-involvement products fall into sectors that most naturally give rise to privacy concerns, such as health and finance. Consumers may not be troubled if Starbucks can link coffee-browsing profiles across an app and a store, but they might feel differently about their blood-pressure profile being linked to their features via facial recognition.Second, as technological capacity improves, the trade-off between personalization and privacy concerns will sharpen. Existing research has emphasized that there are natural trade-offs between a customer's acceptance of personalization and the degree of their privacy concerns and sense of control over their data ([29]; [78]; [85]). Given the natural technological challenges of merely tracking a customer across different touchpoints in their customer decision, as of yet most technological investments have been focused on syncing and tracking. However, once this natural technology barrier has been resolved, firms will soon have to face key decisions about how much personalization they attempt, and how acceptable such personalization will be, given customer privacy concerns. For example, one of the primary goals of matching omnichannel marketing to the customer journey is to link earlier stages in the decision process with prior purchase decisions. However, will customers find it acceptable for firms to remind them of their prior purchase decisions or their product search history across different digital touchpoints?This leads to three major potential challenges for firms aiming to conduct effective omnichannel marketing while being mindful of consumer privacy concerns. The first challenge is that customers may not be willing to allow the focal firm to collect, parse, and sync their data across devices and touchpoints for use in marketing. The marketing literature has emphasized that one way of addressing this natural privacy concern is to improve perceived consumer control over data. Typically, it is the combination of lack of control and perceived privacy intrusion that is most problematic in customers' minds ([78]). Therefore, many managerial solutions to these constraints imposed on omnichannel marketing by customer privacy concerns may come in the form of improving customer control over their data.The second challenge is that customers may not be willing to allow other firms that they interact with in their decision journey to collect, parse, and sync their data across devices and share these data with the focal firm. In general, omnichannel marketing has focused on questions of how to piece together disparate fragments of customer data ([61]), in the absence of privacy concerns. However, as of yet, little research has investigated how firms can share customer data with channel partners in a way that reflects consumer privacy concerns.The third challenge is that regulators may not be willing to allow firms to share, sync, and collect customer data across different firms, devices, and touchpoints. Since May 2018, firms throughout the world have had to grapple with the General Data Privacy Regulation (GDPR), a European Union (EU) regulation designed to ensure that firms document that they have obtained consent from customers to use their data. One of the most striking novelties of this regulation is its global reach. For example, if a Malaysian website served EU citizens, then it is subject to the regulation and needs to ensure that its use of cookies was compliant. Furthermore, penalties for contravening the regulation are large—4% of worldwide turnover. There are already examples of how such regulation has restrained firms' attempts at omnichannel marketing. Firms such as JD Wetherspoon, a restaurant chain, had to take steps antithetical to the ambitions of an omnichannel retailer, such as deleting over 800,000 email addresses and halting email marketing, in anticipation of the regulation ([54]).Although the GDPR is focused on EU data subjects, there is some evidence that even firms based in the United States are choosing to implement its strictures rather than go through the complex process of identifying which website visitors are or are not affected ([55]). By contrast, the new California Privacy Act in the United States could potentially affect U.S. firms directly. Because the California Privacy Act has some data-use restrictions that resemble that of the GDPR, there may be similar negative effects on firms' ability to pursue omnichannel strategies in the United States. However, at the time of writing of this article, the act is still being litigated and its actual effects are uncertain.Another effect of the GDPR for omnichannel marketing has been its impact on firms' ability to engage in probabilistic matching. Probabilistic tracking uses data on the visit (e.g., the IP address, the device used, the browser used, the timing, the location) to predict whether it is the same customer. The GDPR has restricted the collection of IP addresses as potentially personally identifiable information. As such, the regulation has restricted one of the major ways that probabilistic matching is done. It has also given incentives to firms to pursue more deterministic forms of tracking, such as forcing the use of login credentials, which may, in turn, be more privacy-intrusive than probabilistic tracking methods.Many of the potential costs of this regulation for omnichannel markets stem from its focus on obtaining and documenting consent. This means that firms are prioritizing their use of technologies such as customer data platforms for compliance reasons, rather than focusing on the potential for such technologies to provide a more complete picture of a customer or theorizing how that customer might feel about the combination of data the firm is collecting. Customer data platforms are therefore being marketed as a way of tracking the consent status and origins of disparate pieces of information about a customer, rather than their initial aim of enabling seamless omnichannel marketing. It is not clear, however, whether documentation of compliance with the law supplants the ideal use of such technology, which is to ensure that firms track customers across the decision journey in a manner that makes customers feel comfortable. The first column in Table 3 summarizes the key issues related to each of these challenges.GraphTable 3. Privacy-Related Challenges, Remedies, and Future Research. Technological Remedies to Help Protect Customer Privacy in Omnichannel MarketingIn general, the technological frontier on marketing is at odds with maintaining customer privacy. In this subsection, we discuss the source of this tension and offer potential future remedies. Machine learning and predictive analytics privacy remediesRecent advances in machine learning and other predictive technologies are primarily focused on allowing firms to make predictions about an individual customer's future behavior. This contrasts with previous marketing analytics, which have been focused on predicting aggregate behavior. To address privacy concerns while conducting omnichannel marketing, a firm can either try to guarantee not to predict behavior using only an individual's data or, if they do predict behavior at the individual level, try to ensure that these data and predictions are anonymized. For example, rather than storing data about a particular customer, a firm could make predictions about customers' likely purchase path going forward on the basis of the aggregated actions of other customers. Alternatively, a firm could ensure that all data it stores about an individual are anonymized and depersonalized.We argue, though, that eventually privacy in omnichannel marketing will become less a question of where data are stored and more a question of whether a customer feels that the predictions made by data are intrusive. Although predictive analytics can be conducted in a way that focuses on using aggregated, anonymized, and depersonalized data, it is not clear that it directly addresses customer privacy concerns, even if it is compliant with privacy regulation. For example, imagine that a customer is browsing a web supermarket storefront, and a predictive analytics suite that uses privacy-compliant aggregated and anonymized data that associates mobile data with desktop website–based data predicts that, in line with her browsing behavior, she is also likely to be interested in contraception. The customer may still find such a suggestion intrusive, even though the suggestion itself was made using privacy-compliant analytics.As another example, in the world of adtech, Data Republic is a data exchange platform that allows organizations to deidentify and match data sets without personally identifiable information ever having to leave the firm's secured servers. Again, privacy compliance is focused on the question of how and where data are stored and how anonymous the data are when stored. Blockchain privacy remediesBlockchain technology may provide customers better (or at least decentralized) ownership rights over their data. In advertising, an example of this is Brave, a ""privacy browser"" that is combined with blockchain-based digital advertising. The underlying idea is that Brave users will own the rights to their data and share in the profits of firms advertising to them ([14]). The role of blockchain technology is to allow the immutability of ""basic attention tokens,"" which is the currency by which Brave users are rewarded for their attention to advertising. Although Brave has solved some concerns, recently it has been criticized for trying to monetize its users' attention through steering their browsing behavior ([72]).Although this example is focused on advertising rather than full omnichannel marketing, it does illustrate the potential challenges of using blockchain technology to resolve privacy concerns in a context where multiple firms are trying to track users across multiple touchpoints. The challenges that exist between blockchain technology and data privacy requirements include, at a minimum, the following three use cases: ( 1) different perspectives on anonymity and pseudonymity, ( 2) identification of data controllers and data processors in various blockchain technology implementations and how they affect the applicability of various data protection and privacy laws, and ( 3) reconciling transaction immutability and data preservation in blockchain applications with individuals' rights.First, it is often believed that transparency afforded by blockchain-related solutions may help mitigate such consumer concerns by giving consumers information on how advertisers have used their data ([30]; [84]). Blockchains are often designed so that all transactions are visible to everyone. They are pseudonymized, meaning that only addresses are visible on the blockchain, and anyone can get an unlimited number of addresses. Still, even in this system, it is possible to identify individuals by examining transactions linked by the addresses ([35]) and statistically predicting the characteristics and identity of an individual by combining these transaction data. Furthermore, it would be very difficult to prevent the visible information from being copied and used in a different way on a different system. Therefore, current blockchain technology that emphasizes visibility and the reduction of asymmetric information may not prevent marketers from selling customer data.Second, blockchain technology's distributed peer-to-peer network architecture can also put it at odds with data privacy laws such as the GDPR and California Consumer Privacy Act. This is because a law such as the GDPR relies on the idea of centralized controller-based data processing or a distinct firm that oversees and manages data processing. By contrast, blockchain is explicitly decentralized, and part of its merit is that there is not a single controlling firm or body. This disconnect can make it difficult to reconcile current data protection laws with blockchain's other core elements, such as the lack of centralized control, immutability, and perpetual data storage. Regulatory guidance on reconciling this and other potential conflicts is currently a work in progress.Finally, many of the privacy concerns associated with blockchain stem from the fact that its major virtue is to ensure data integrity and ensure that data are immutable. However, preserving data in an immutable form is itself a privacy challenge.As we have discussed, blockchain technology can be either permissionless or permissioned. Typically, permissionless blockchains are explicitly decentralized without a governing or controlling body. One potential solution to some of these challenges of protecting privacy in a blockchain environment is to move to permissioned blockchains, such as the IBM technology used by Walmart. IBM Food Trust is a permissioned blockchain that Walmart's suppliers of leafy greens are required to use. However, unlike the more traditional permissionless blockchain, simply participating in the blockchain does not provide any visibility into the data recorded there. Walmart has access to all the information, but suppliers can see only the information they have provided themselves. Such blockchain-based systems provide only constrained transparency, however. The information in the blockchain is more transparent to Walmart than the previous record-keeping methods. The suppliers obtain more information than before, but the system is not fully transparent for them. In other words, concerns about data visibility can be addressed by moving blockchain toward a permissioned format, which loses some of the unique benefits of decentralized blockchains that have often attracted blockchain enthusiasts. However, it is not clear that the permissioned blockchain format addresses issues of immutability of data or the fact that blockchain is essentially a technology focused on preserving and ensuring the integrity of data, which naturally puts it at tension with privacy. Future Research Investigating Customer Privacy in Omnichannel MarketingOur discussion highlights that although it is possible to use tools such as machine learning and blockchain to address privacy concerns, the use of these technologies creates different privacy concerns. This insight suggests fruitful avenues for future research. We highlight several possibilities.First, is there a way of using predictive analytics in a manner that is conscious of customers' likely privacy preferences? For example, is it possible to build a predictive model that ensures that any suggestions made in an omnichannel context are never likely to be perceived as intrusive? To achieve this goal requires a deep understanding of what customers consider a privacy-invasive touchpoint or suggestion in an omnichannel context ([ 7]). We highlight that this kind of research—whether it be done through surveys, data analysis, or A/B testing—is going to be crucial to ensure that predictive analytics are not just privacy-compliant but actually privacy-conscious. Toward this direction of future research, [52] build on the principle of location data obfuscation to provide a framework that allows, for example, a reduction in a firm's probability of being able to infer a customer's home address, with no reduction in actual targeting accuracy for advertising.Second, can research uncover ways to emulate existing blockchain-based ecosystems in an omnichannel context? For example, can a firm use blockchain to create a token that establishes a currency allowing the consumer to be rewarded for sharing their data as a part of an omnichannel marketing effort? More ambitiously, is there a way that multiple firms can coordinate around a single-token-based scheme to help kick-start a larger ecosystem? As with any time firms work together, there will be interorganizational challenges, especially if the firms are competitors and these proposals involve sharing data. These interorganizational challenges may lead to useful theoretical modeling opportunities for marketing academics. For example, theory work could examine what would give rise to incentive-compatibility issues in a blockchain-fueled data exchange system in an omnichannel context, which would uncover the likelihood and drivers of firms being willing to share data with competitors and channel partners. This would illustrate the types of industries, products, and patterns of consumer behavior offering the largest incentive-compatibility issues in terms of data sharing.Third, how successful are adtech initiatives that have helped omnichannel marketers become privacy-regulation compliant? Are they inherently just a cost that interrupts the accurate processing of information, or are there benefits in terms of enhanced consumer trust of that firm? For example, if a firm offers an array of privacy-compliance tools, does it actually have a measurable effect on the consumers' relationship to the firm, in terms of measurable purchase behavior or measured attitudinal change? The recent spate of privacy regulation, and in particular regulation in California, has led to a large number of startups that are trying to help firms comply with new regulations ([40]). These vendors span functionalities such as activity monitoring, assessment management, consent management, data discovery, data mapping, deidentification, and privacy management. Each of these functionalities is likely to be core to a privacy-compliant omnichannel future. Yet these are also technologies whose role the academic marketing community knows little about. It strikes us that useful partnerships between academics and firms in this space could help provide an early assessment of the usefulness of such tools, and how to improve them, for firms, consumers, and regulatory compliance.Fourth, as discussed in the previous section, recent developments in machine learning aim to provide privacy controls. For example, ""federated learning"" trains a machine-learning algorithm across multiple decentralized devices such as mobile phones that hold local data samples, without exchanging the data. These leakages can stem from loopholes in collaborative machine-learning systems, whereby an adversarial participant can infer membership as well as properties associated with a subset of the training data. [44] propose a blockchained federated learning (BlockFL) architecture, where the local-learning model updates are exchanged and verified using a blockchain. Might such developments temper privacy concerns and lead to more efficient omnichannel marketing programs?Fifth, public policy has thus far focused on the deleterious effects of machine-learning-induced algorithmic biases in the form of racial or gender discrimination. Scant research or policy has examined the use of personal information in algorithms. For example, does greater transparency into customers' path-to-purchase journey, even with the explicit consent of the customer, result in the unintended consequence of giving omnichannel firms room to price discriminate efficiently and, in doing so, erode consumer welfare? This would be particularly problematic if these data led groups of different socioeconomic backgrounds or different races to pay different prices. As a starting point, it would be useful for research to document the extent to which having more individualized data leads to more price discrimination and, if so, whether that price discrimination appears to be associated with any historically disadvantaged groups. ConclusionHow does omnichannel marketing differ from how firms have interacted with consumers in the past? In this article, we argue that, to realize the full potential of omnichannel marketing, firms need to track the same consumer across multiple channels. Obtaining such a 360-degree view of the customer experience would require hitherto unimagined consumer tracking capacity by firms. We have highlighted the root causes of three key sources of informational challenges that might prevent firms from realizing the potential of omnichannel marketing—data access/integration, marketing attribution, and protection of consumers' privacy—and discuss how emerging technologies such as machine learning and blockchain can help address these challenges. We establish that while these technologies have promise as solutions, they also create new challenges and opportunities. In addition, we discuss fruitful avenues for future research in each of the three challenge areas. Next, we highlight several possibilities of future research that integrate the three areas.First, obtaining a 360-degree view of the customer experience, on the one hand, while maintaining customer privacy, on the other, seem to be at odds. However, a firm might need only a subset of information on customer touchpoints to make effective inferences about attribution. If some of these data that firms might not need for attribution are also those for which customers have serious privacy concerns, the firm could collect only the subset that is useful for its internal purposes, thus giving customers a semblance of privacy. Identifying such data represents a potential win-win and therefore is a useful area of research. This is likely a process that will need to be ongoing as consumer education and government regulation increases.Second, related to the previous point, are there some types of data that are only needed in the short run for attribution purposes about which customers have privacy concerns? Identifying such data is a useful area of research from a public policy perspective, as countries could mandate potentially attractive regulations limiting retention of such data.Third, while more information is always beneficial to the firm from the perspective of managing customer experience, there may be diminishing returns. Therefore, might it be worthwhile to quantify the incremental benefit of additional data or data from multiple sources for attribution? If we believe it is the combination of data that represents the greatest privacy risk, it would be beneficial for future research to identify instances where there are swift diminishing returns to incremental data in companies, as these data could be removed from regular collection.Fourth, could there be a marketplace for consumer data that results in fair valuation while preserving privacy, thus creating a win-win situation? Many consumers are increasingly willing to share their personal data (e.g., their location) with brands in return for some economic incentives (e.g., discounts). This comes from the belief that their data are their asset, and just like a property right, they should be able to exchange this asset with brands for monetary compensation from marketers ([39]). Some consumers, however, hesitate to participate because they believe that brands and marketers may not appropriately compensate them for their data. Future research could consider how platform design can inspire consumer confidence and how various mechanisms, such as auction, might be useful in clearing such a market.Fifth, can blockchain-based technologies be used in facilitating the market for customer information? The hope is that when such a blockchain-based marketplace emerges, consumers will have a transparent overview of how their data are valued and which brands might be willing to enter an exchange with them. It would be beneficial for future research to identify the hurdles (both from consumers and firms) to participating in such markets, and how to overcome them.In summary, our thesis is that while omnichannel marketing promises to open up new opportunities for firms, firms need to be cognizant of the tension between obtaining a 360-degree view of the customer (and the challenges therein) and alleviating concerns about loss of privacy. We hope that our article helps spearhead future research solving these challenges in omnichannel marketing. " 34,"Investigating the Effects of Excise Taxes, Public Usage Restrictions, and Antismoking Ads Across Cigarette Brands"," The prevalence of strong brands such as Coca-Cola, McDonald's, Budweiser, and Marlboro in ""vice"" categories has important implications for regulators and consumers. While researchers in multiple disciplines have studied the effectiveness of antitobacco countermarketing strategies, little attention has been given to how brand strength may moderate the efficacy of tactics such as excise taxes, usage restrictions, and educational advertising campaigns. In this research, the authors use a multiple discrete-continuous model to study the impact of antismoking techniques on smokers' choices of brands and quantities. The results suggest that although cigarette excise taxes decrease smoking rates, these taxes also result in a shift in market share toward stronger brands. Market leaders may be less affected by tax policies because their market power allows strong brands such as Marlboro to absorb rather than pass through increased taxes. In contrast, smoke-free restrictions cause a shift away from stronger brands. In terms of antismoking advertising, the authors find minimal effects on brand choice and consumption. The findings highlight the importance of considering brand asymmetries when designing a policy portfolio on cigarette tax hikes, smoke-free restrictions, and antismoking advertising campaigns.","While the goal of marketing is usually to boost purchase rates and strengthen relationships between consumers and brands, countermarketing is an increasingly common strategy for reducing the consumption of ""vice"" goods such as cigarettes. Countermarketing activities may include excise taxes that increase consumer costs, usage constraints that restrict public consumption, and advertising that highlights product dangers. Cigarette countermarketing has seemingly been effective, as U.S. smoking rates have dropped from 44% in 1950 to 14% in 2011 ([16]). In addition, countermarketing is now increasingly applied in other categories that may create health risks, such as soft drinks and fast food ([36]).A notable feature of many ""vice"" categories is that they are dominated by very strong or high-equity brands. For example, the Interbrand Top 100 brands list has often included Coca-Cola, McDonald's, Budweiser, and Marlboro. However, economic and public health research on countermarketing effectiveness has largely ignored the role of brands. This is an oversight in that the perceived importance of branding and marketing is demonstrated by advocacy groups' and regulators' efforts to limit brand advertising. Although almost all previous branding research has focused on the value of strong brands in forming and maintaining brand–consumer relationships, it is reasonable to speculate that strong brands might also affect the efforts of advocacy groups and regulators to disrupt these relationships and reduce consumption.The marketing literature discusses a variety of benefits that accrue to strong brands. Strong brands may have advantages in terms of increased customer loyalty, diminished price sensitivity, wider distribution, heightened consumer awareness, and other benefits ([ 1]; [ 4]). For example, brands may provide symbolic benefits that increase the value of public consumption, and there may be strong psychological bonds between a brand and its customers (Fournier 1998). Furthermore, stronger brands might enjoy greater channel power that results in wider distribution and customer awareness. A prominent example of an effort to reduce brand power is the Australian government's attempt to limit the influence of branding through mandating plain packaging without any iconography for tobacco products starting in December of 2012 ([15]; [59]).An important aspect of the literature on branding is that brand strength may be manifested through different mechanisms. Critically, the different dimensions of brand strength may protect brands against or make brands more vulnerable to specific countermarketing tactics. For instance, if brands provide benefits by conveying status or glamor, the most effective regulations may be different than if brand strength involves deeper psychological bonds that influence loyalty or price sensitivity. This insight highlights the importance of including brand-level effects for alternative countermarketing activities in an empirical specification.Our research investigates how the interplay between branding and countermarketing activities influences consumers' consumption of cigarettes. The tobacco industry provides an important and useful context for our research for several reasons. First, tobacco consumption causes significant economic costs and adverse health consequences. Cigarette smoking has been estimated to cause 480,000 premature deaths each year in the United States, and it imposes health care costs and productivity losses of about $300 billion each year ([16]). Second, this industry has been the target of a significant amount of countermarketing activities that affect consumer decision making. For instance, taxes increase consumer prices, smoking bans make public consumption less convenient, and educational advertising campaigns highlight adverse health consequences. In addition, as countermarketing tactics are largely determined at the state level in the United States, there is a significant variation in policies across states. This variation facilitates identification of the effectiveness of different countermarketing techniques. Third, advocacy groups and regulators are currently using experience from the tobacco category to guide efforts in other categories. For example, there is significant interest in using countermarketing techniques to reduce obesity ([36]; [38]; [51]; [52]). Fourth, differences in brand equity in the cigarette category afford an opportunity to study the interplay between countermarketing techniques and brand power.Vice categories such as cigarettes are also of interest because they highlight the existence and incentives of diverse stakeholders within a category. These diverse perspectives are relevant to consider because groups with different goals may adjust strategies in response to different regulatory approaches. For instance, the literature on countermarketing ([18]) has primarily focused on the effectiveness of regulations in reducing smoking. This perspective is concerned with identifying successful tactics for regulators by tallying smoker quit rates. While these analyses are important, they are incomplete. In addition to quit rates, governments may be interested in the impact of policies on tax revenues, and consumers may suffer economic consequences.Beyond regulators and consumers, brand manufacturers are often overlooked as relevant participants in the category. This omission affects our knowledge on two aspects of this issue. First, firms wish to select the most effective strategies for their environment. Second, firms and brands vary in terms of their characteristics, distribution strength, and awareness. These factors may lead to different regulatory tactics with asymmetric effects across the category. For example, some brands may have pricing or distributional power that allows them greater flexibility in managing the tax pass-through to consumers. Policies that limit the public consumption of cigarettes may also be relevant because cigarettes have long been considered a prototypical example of a badge product, one used to project social status ([ 8]). Thus, prohibitions on public consumption may vary in effectiveness on the basis of brand strength. How different dimensions of brand strength influence the effectiveness of countermarketing techniques remains an open research topic.To investigate the relationship between branding and countermarketing, we assemble a data set that includes a consumer panel of cigarette purchases for a six-year period from 2005 to 2010, retail scanner data from 2006 to 2010, and a comprehensive data set on state-level cigarette taxes, state-level smoke-free restrictions, and national antismoking advertising campaigns. We conduct our analysis using a multiple discrete-continuous choice model of smokers' monthly brand and quantity decisions. Our empirical specification is designed to evaluate if and how cigarette excise taxes, smoke-free restrictions, and antismoking advertising campaigns influence cigarette purchase decisions asymmetrically across a variety of brands and composites of brands based on price tier. Our research also includes an analysis of tax pass-through, highlighting the role of brand positioning and channel characteristics. The pass-through analysis is used in a series of counterfactual simulations that assess the full effects of alternative countermarketing techniques across different stakeholders.Our results show that the effects of antismoking interventions vary significantly across brands. For example, the demand model reveals that Marlboro is relatively less affected by tax increases but relatively more affected by usage restrictions. The resistance to taxes is driven by Marlboro's ability to pass through less of the tax increases than most other brands. This effect may be due to market share based on economies of scale or distribution strength that leads retailers to limit price increases of their highest-volume brand. In the case of usage restrictions, results show that high-equity brands incur more negative effects, and our speculation is that public prohibitions make it more difficult for consumers to garner symbolic or image-based benefits through consumption of high-equity brands. In regard to antismoking advertising, we find that these communications have relatively little effect overall but do have a slightly above-average impact on Marlboro. In terms of category evolution, our results offer an explanation for why Marlboro's relative market share has increased dramatically over time. During our observation window, cigarette excise taxes almost doubled. This aggressive tax policy has shifted demand toward the category leader.We conduct a series of policy experiments to assess the differential effects of alternative countermarketing policies across stakeholders including regulators, consumers, and brands. We find that a 100% tax increase yields a 30% increase in quit rate, but it imposes significant costs to consumers and only increases tax receipts by about 28%. In contrast, an aggressive smoke-free policy increases quit rates by 9% and reduces tax revenues by 6%. With usage restrictions, consumers may experience inconvenience and reduced symbolic benefits but do not incur economic costs. In general, we find that stronger brands tend to be more resilient to tax increases and more susceptible to usage restrictions. Collectively, our simulations show that the choice of countermarketing tactics greatly impacts relative quit rates, consumer costs, government revenues, and brands' market shares. Conceptual FoundationsTo frame our research, we consider selected literature on countermarketing from the fields of economics, public health, and marketing. Economics and public health have significant traditions of studying countermarketing effectiveness, and these disciplines typically rely on surveys rather than actual customer behavior; therefore, marketing issues are usually neglected. The marketing literature focused on tobacco countermarketing has used a variety of experimental and empirical methods to examine consumer response to countermarketing. In addition to research on tobacco control, we also review literature related to the possible interactions between countermarketing and branding. In our review, we put explicit focus on branding topics that may lead to asymmetric effects of countermarketing tactics for stronger versus weaker brands. Antismoking InterventionsThe economics literature on smoking has relied on large-scale surveys and reduced-form models to investigate the role of individual countermarketing tactics on consumption ([17]; [23]; e.g., [10]). Of the various countermarketing instruments, excise taxes and pricing have received the most attention in the economics literature. Cigarette excise taxes are implemented at the pack level and are included in retail prices ([21]). These taxes typically include a federal and state component. In general, researchers have found that excise taxes have a significant impact on smoking rates. The price elasticity of cigarette demand is generally found to be about −.4 (see [18]).Antismoking advocates have been increasingly successful in implementing ""smoke-free"" restrictions such as prohibitions against smoking in bars, restaurants, and public places. These interventions reduce convenience and increase time costs by forcing smokers outdoors. Smoke-free restrictions have increased in prevalence over time. In the year 2000, approximately 50% of the U.S. population was potentially affected by clean-air smoking policies. By 2008 this percentage had grown to over 70%. Research on smoke-free air policies has yielded mixed results. [25] find that voluntary workplace restrictions lead to minor reductions in smoking. [14] and [ 3] find that smoke-free laws have no impact on smoking behavior. However, these studies all rely on self-reports collected via surveys.There is a significant literature on the impact of marketing communications on cigarette purchases. For example, [47] find that cigarette brand advertising elasticity is.28. Several marketing studies provide lab-based experimental evidence on the effectiveness of antismoking ad messages ([ 6]; [44]; [45]). For example, [44] use experimental methods to study how smoking scenes in movies elicit different emotional reactions depending on whether an antismoking message was shown before the film. Other research attempts to quantify the relationship between levels of antismoking advertising and quitting behaviors. [60] show that an increase of 390 monthly gross rating points leads to a.3% decline in smoking prevalence in Australia. [26] claim that an increase of 5,000 gross rating points annually increases the odds of quit attempts by 21% in New York City.There is also a growing marketing literature that evaluates consumer-level purchasing data. In terms of pricing and taxes, [20] examine the effect of Marlboro's permanent 1993 price cut on brand choice; [31] investigate the elasticity of demand for temporary versus permanent price adjustments. Gordon and Sun find that short-term price elasticity is smaller than the long-term elasticity. While these marketing studies illustrate the roles of pricing and promotion on brand-tier choice and incidence, they consider only limited elements of countermarketing. [61] investigate the relative effectiveness of cigarette excise taxes, antismoking advertising, and smoke-free restrictions on category sales. They examine the consequences of the countermarketing mix on product substitution among products with varying nicotine levels but do not consider branding effects. In general, this literature pays little attention to the issues of branding and consumer loyalty. In a notable exception, [15] use secondary data to measure the causal effect of the Australian antibranding legislation at both the cigarette category level and the brand-strength tier level. They find that the elimination of branding elements results in greater price sensitivity to increases for premium and mainstream brands.Some research investigates the impact of countermarketing on other categories. [30] measure the cross-category spillover effects of selling tobacco products on the revenue generated by nontobacco categories. In addition to tobacco, there is growing interest in using countermarketing techniques to reduce obesity ([36]; [38]; [51]; [52]). Branding and Asymmetric ResponseThe literature on smoking cessation has largely ignored the impact of branding on efforts to reduce cigarette consumption. This is an oversight given that marketing researchers have found that brand–consumer relationships have significant effects on consumer decision making ([ 5]; Fournier 1998; [35]). The Australian plain tobacco packaging policy has yielded significant results related to the importance of packaging and branding. For example, [24] find that the elimination of branding elements reduces the perceived attractiveness of cigarette packages and affects brand choice. They find that eliminating branding reduces consumer perceptions that the look of their cigarette package ""says something good about them"" or ""is fashionable."" In addition, [59] find that the health-oriented warnings mandated by the Australian policy result in an increase in smokers concealing or hiding packages.A negative link between removing visual branding elements and consumption intentions is intuitive. Cigarettes have often been referred to as badge products, as cigarette consumption frequently involves displays of branded packages in public settings such as bars and nightclubs. There are multiple streams of the marketing literature relevant to the value a badge brand may give a consumer. [33] discusses how brands can act as vehicles for expressing psychological and social traits. [34] suggests that brands provide a means for consumers to express their self-concepts. For instance, consumers might choose Marlboro to associate the rugged brand image with themselves ([ 2]).Brands can also serve as a focal point for communities of consumers ([39]; [42]). Brand communities are groups largely based on admiration and preference for a focal brand. For these communities to exist, consumption and brand preferences must be publicly expressed so that members can identify each other. A notable example of a consumption community built around a cigarette brand was Marlboro Lights in the U.K. market. The economics literature also includes work that emphasizes the social-signaling benefits of conspicuous consumption ([ 7]).The role of brands as instruments for expressing self-identity or as a focal point for a consumption community is potentially relevant to the effectiveness of smoke-free air policies. These policies are primarily designed to limit cigarette consumption in public venues. By limiting public consumption, these policies may limit the value that brands provide to consumers. However, there remains an outstanding question as to whether the impact of such policies will vary across types of brands if some brands provide greater symbolic benefits.Brand strength may also operate through other mechanisms that affect how tax increases are passed through to consumers by the retailer. Specifically, tax increases may have differential effects across brand price tiers due to differences in price sensitivity and distribution channel power ([ 1]). First, higher-equity brands may be more able to pass through greater percentages of tax increases simply because consumers are less price-sensitive for these brands. In fact, given that taxes will tend to shrink the entire category, it is possible that stronger brands may even choose to implement price increases to make up for lost volume. Second, if stronger brands charge higher prices than other brands, then imposing constant per pack taxes will result in lower percentage price increases. Third, awareness and broad distribution may offer benefits in terms of larger market shares and economies of scale—advantages that can accrue to higher-equity brands if retailers wish to maintain prices on especially important brands within a category.The addictive nature of cigarettes carries its own implications for the design of a consumer demand model. Because nicotine is an addictive substance, much of the repeat buying of cigarettes is driven by physical addiction. However, it is also possible that some type of attitudinal loyalty exists in the category. The key point is that in a category such as cigarettes that includes powerful brands, purchase feedback effects such as brand loyalty, satiation rates, and addiction effects need to be included in any empirical specification. Summary and Model-Specification ImplicationsThe review of the existing literature on antismoking effectiveness highlights several salient empirical issues and research gaps. Researchers have investigated the effects of taxes, usage restrictions, and negative advertising. However, these variables have seldom been evaluated simultaneously, and there is still debate about the effectiveness of interventions such as smoke-free restrictions. Therefore, it is critical that any empirical specification include the complete set of countermarketing tactics.The discussion of branding and consumer issues highlights key considerations for an empirical specification. In terms of branding, the cigarette category includes many brands that vary in terms of price, market share, brand personality, and distribution power. As discussed previously, there are theoretical reasons to believe that different types of brands may be differentially affected by alternative policy interventions. There are also important aspects of brand loyalty that need to be incorporated in an empirical specification. For example, brand loyalty and other purchase feedback effects may be relevant for modeling brand choice. Even basic elements of consumer choice such as whether consumers purchase single or multiple brands need to be considered. DataOur research objectives necessitate the use of multiple data sets. To understand consumer-level decisions about brand choices and quantity consumed over time, we use panel data of individual smoker purchases. Given the large number of brands in the category, we supplement the individual-level data with market-level data to identify the price environment faced by consumers. To study the effects of countermarketing, we assemble information on taxes, antismoking ads, and smoke-free restrictions from governmental agencies and nongovernmental organizations. Smoker Panel Data and Retail Scanner DataThe individual smoker panel for our study is from the Nielsen Consumer Panel for the six-year period between January 2005 and December 2010.[ 7] The Consumer Panel provides each household with an optical scanner for scanning the barcodes of all consumer packaged goods they purchase, regardless of the outlet. The data, therefore, include purchases from supermarkets, convenience stores, drug stores, gas stations, and other outlets. This broad coverage is important because, unlike the product categories often studied in the literature (i.e., those primarily sold in supermarkets), smaller retail outlets account for a significant proportion of cigarette sales.We select a sample of smokers for our study by applying the following ordered criteria: ( 1) keep only single smokers that stayed in the Nielsen Consumer Panel for all six years, ( 2) keep smokers that made at least 20 cigarette purchases, and ( 3) keep smokers that had cigarette purchases in 2005, the beginning of our observation window. The three selection criteria result in a panel of 422 single smokers that were potentially in the process of quitting smoking or did quit smoking over the six-year period. We use 2005 as an initialization period and the years 2006–2010 for estimation.Table 1 shows that approximately 22% of smokers quit smoking, where quitting is defined as individuals with no purchases during the final 12 months of the observation window. The median cigarette purchase interval is about once per month. On average consumers purchase 23 packs and spend an average of $75 on cigarettes per month. To ensure representativeness, we cross-validate our sample's demographics and cigarette consumption patterns against the 2009–2010 CDC National Adult Tobacco Survey. We show in Web Appendix W1 that the 422 single smokers in our estimation sample had similar demographic distributions[ 8] and consumption levels as those in the CDC National Survey.GraphTable 1. Smoker Cigarette-Purchase Summary. 20022242921994570 Notes: Quitting is defined as no cigarette purchase during the final 12 months of the observation window.A primary benefit of the Consumer Panel is that we can observe consumers' brand and quantity choices. However, the 422 smoker panelists purchased more than 170 cigarette brands. We use the following approach to facilitate the analysis: First, we select the top four cigarette brands (in terms of purchase volume) within our purchase panel: Marlboro, Basic, Winston, and Virginia Slims. Next, we aggregate the remaining brands into three categories—premium, mainstream, and economy—on the basis of average national retail price (see Web Appendix W2).To implement the brand categorization scheme, we obtain information on cigarette prices and quantities sold at the Universal Product Code/store/week level between January 2006 and December 2010 from the Nielsen Retail Scanner Data. We construct brand-level data at the monthly level for each store by aggregating the Universal Product Code–level data at a set of 3,874 retail stores across 46 states. These stores are selected because they have complete price and sales information for the four brands and three price-tier categories. Table 2 presents the average prices of the four individual brands and the three categories of brands. The average price differentials between premium and mainstream brands, and between the mainstream and economy brands, are $.70. Marlboro, Basic, and Winston are priced similarly to the mainstream brand category, while Virginia Slims is priced similarly to the premium category.GraphTable 2. Cigarette Brand Prices and Purchases. 30022242921994570 Notes: Standard deviations are in parentheses. ""Monthly brand choice probability"" refers to the probability a brand will be chosen in the estimation sample, and ""monthly packs"" refers to the number of packs conditional on purchase. The multiplication of brand choice probabilities and conditional brand purchase quantity provides the unconditional brand shares among the seven brands.Table 2 also includes data on brand choice and consumption. In Table 2 and the other exhibits in this section, all results related to price are from the 3,874-store Retail Scanner Data, and all results related to brand choice and consumption are calculated using the 422 smokers in the Consumer Panel. In terms of choice, the mainstream category has the highest monthly choice probability at 20%, followed by Marlboro at 15%, economy brands at 13%, and premium brands at 12%. Basic, Virginia Slims, and Winston have choice probabilities of less than 6%. Conditional on brand choice, smokers typically purchase in the range of 20 to 30 packs per month. There is some variation in the average brand purchase quantity. Winston and the economy brands are purchased in slightly larger quantities. The multiplication of brand choice probabilities and conditional brand purchase quantity provides the unconditional brand shares among the seven brands.The figure in Web Appendix W3 gives additional insight by illustrating the distribution of consumption levels and brand shares for different monthly consumption levels. It shows that conditional on buying any cigarettes, 28% of smokers buy more than 30 packs per month, 22% buy between 20 and 30 packs, 28% buy between 10 and 20 packs, and 22% purchase fewer than 10 packs. Conditional on monthly purchase quantities, the relative brand shares across the seven brand categories vary. Notably, Marlboro captures a substantial share at all levels of consumption. This implies that Marlboro is the dominant brand among both casual and regular smokers.Web Appendix W4 presents data related to brand loyalty and switching. If we define the category in terms of the seven brands and brand categories, Web Appendix W4a shows that over the five years of the data window, 30% of smokers stick with one brand, 28% have purchased only two brands, and about 41% have purchased more than two brands.Web Appendix W4b explores whether multiple brand purchases occur within a month or over time. In 89% of months, smokers purchase only one out of seven cigarette brands. In the other 11% of months, they purchase two or more cigarette brands. Therefore, although the majority of brand switching happens over time, multibrand purchasing within a month is still meaningful. Web Appendix W4c shows that 60% of smokers engage in multiple brand purchases within a month at some point over the observation window. This pattern suggests a need for our demand model to accommodate multiple brand purchase and quantity decisions within a decision period. Antismoking MeasuresOur investigation's critical interventions are countermarketing tactics such as cigarette excise taxes, smoke-free restrictions, and antismoking advertising. Figure 1 shows the evolution of cigarette purchases for the sample of 422 single smokers and the three countermarketing programs' levels over time. Specifically, Figure 1, Panel A, shows that cigarette consumption declines over time for the sample. The average monthly purchase quantity drops from 20 packs in 2006 to 10 packs in 2010.Graph: Figure 1. Purchased quantities and antismoking techniques over time.Notes: Panel A illustrates the unconditional number of packs per month. In Panel C, smoke-free restrictions are enforced in part or all of the eight locations including restaurants, bars, hospitals, private workplaces, government workplaces, grocery stores, hotels, and motels.Cigarette excise taxes are from the ""Tax Burden on Tobacco"" report ([43]), which collects detailed information on federal, state, and local tax rates and effective dates. Figure 1, Panel B, and Table 3 show the evolution of the taxes faced by panelists. The jump in taxes during 2009 is from an increase in the federal tax from $.39 to $1.01 per pack in April 2009. The other changes in taxes are due to changes in state and local taxes.GraphTable 3. Antismoking Techniques Summary. 40022242921994570 Notes: Smoke-free restrictions are enforced in part or all of the eight locations (restaurants, bars, hospitals, private workplaces, government workplaces, grocery stores, hotels, and motels).To measure smoke-free restrictions, we collected smoke-free air policy information for eight common venues defined as restaurants, bars, hospitals, private workplaces, government workplaces, grocery stores, hotels, and motels from the CDC's state tracking studies. In each venue, smoke-free restrictions are assigned one of two values: 0 for no restriction and 1 for a complete restriction. We sum the number of smoke-free restrictions in the eight venues to describe a state's smoke-free restriction level. Figure 1, Panel C, shows the evolution of smoke-free restrictions. Smoke-free restrictions dramatically increased between 2006 and 2008.We also obtained the U.S. monthly spending on antismoking campaigns from Kantar Media. Figure 1, Panel D, shows nationwide monthly spending on antismoking advertising. This figure highlights a significant antismoking advertising campaign that accompanied the federal tax hike in 2009. Overall, expenditures on antismoking ads averaged $535,932 per month over the observation period.We use zip codes to match the taxes and smoke-free restrictions to each smoker. For simplicity, we assume that a smoker purchases only from stores located in the same state where they live and that match the federal, state, and local cigarette excise taxes, respectively. Smoke-free restrictions are matched to each smoker on the basis of the state where they live. The 422 single smokers in our estimation sample cover 46 states. There is substantial variation in the two tactics across states and over time. At the start of our observation window, the tax per package varied from a low of $.46 in South Carolina to a high of $3.30 in New York. At the end of 2010, tax per package varied from $1.18 in Missouri to $6.86 in New York. In terms of smoke-free restrictions, there were relatively few restrictions at the start of the data window. However, by the end of 2010, 19 of the 46 states had complete smoking bans in all eight venues.Model-free evidence related to the relationship between countermarketing and branding is also possible. For example, based on the Nielsen retailer scanner data, the figure in Web Appendix W5 shows that every state increased its per pack tax rates over the five-year observation window and that the market share of Marlboro also increased in almost all states over the five years. For example, in South Carolina, Marlboro's market share increased by 51% as the state per pack tax rates increased by $4.47. Web Appendix W5 also plots the distribution of Marlboro's market shares by year and shows increases over time in response to greater taxes. These analyses suggest asymmetric effects of antismoking techniques across brands, with the market leader consistently gaining share as taxes increase.[ 9] ModelWe use a multiple discrete-continuous choice model ([12], [13]; [50]; [54]) to model smokers' monthly cigarette purchase-quantity decisions within the seven brand categories. The model provides a parsimonious structural approach for investigating the purchase of multiple combinations of brand choices and quantities. The analysis quantifies the effect of the three antismoking techniques on brand choices and purchase quantities while allowing for the possibility of asymmetric effects across brands.As there are J brands in the choice set, a smoker i can xijt of brand j in period t. We drop the subscripts i and t and specify the monthly cigarette purchase utility to a smoker as the sum of the utilities obtained from purchasing xj packs of each cigarette brand asU(x)=ϕ1ln x1+∑j=28γjϕj{ln(xjγj+1)},1where U(x) is a quasiconcave, increasing, and continuously differentiable function with respect to the purchase quantity vector x ( xj≥0 for all j). The first good x1 is the outside good that is always consumed. Term ϕj is the baseline marginal utility that represents the utility of choosing brand j at the point of zero purchase:∂U(x)∂xj=ϕj(xjγj+1)−1.2The marginal rate of substitution between any brand k and l at the point of zero purchase of both goods is ϕk/ϕl . We parameterize smoker i's baseline utility of purchasing brand j in period t asϕijt=exp[β0i+Brandjβ1ij+β2ij×Brandj×SFit+β3ij×Brandj×ln(1+AntiAdSt)+β4iBrandLoyalijt+∑t​Yeartβ5,t+∊ijt],3where the Brandj terms are brand dummies, and β1ij is smoker i's intrinsic preference for brand j. The term SFit represents the level of smoke-free restrictions faced by smoker i in period t. We allow the impact of smoke-free restrictions to vary across brands (β2ij). The term AntiAdS t denotes the antismoking national advertising stock that a smoker is exposed to in period t. We let the antismoking advertising stock evolve as AntiAdSt=AntiAdt+ρ1AntiAdSt−1 .[10] The effect of antismoking advertising also potentially varies across brands (β3ij). The term BrandLoyalijt is a dummy variable indicating purchase or not of brand j in the last period. The coefficient of BrandLoyalijt represents brand choice state-dependence. We also include year fixed effects in the baseline utility to account for any trend in smoking. The term ∊ijt is an extreme value distributed error term that is i.i.d. with a scale parameter of σ. The baseline utility for the outside good ϕi0t is normalized to 1.Term γ in Equation 2 is a satiation (or translation) parameter.[11] A larger γ value indicates a stronger preference (or lower satiation) for cigarette brand j. All else equal, smokers would purchase more of brand j if γ is larger. Including a satiation parameter specific to brand j allows for the model to yield corner solutions where only brand j is chosen. We parameterize the satiation term as γijt=δ1ij+δ2iln(1+Adctit) . The satiation parameter is brand-specific and is also a function of past cigarette consumption and addiction. We formulate a cigarette addiction stock as Adctit=∑j​xijt−1+ρ2Adctit−1 , where the addiction decay ρ2 is evaluated with a grid search of the.1 intervals from 0 to 1. This formulation is consistent with [ 9] theoretical model of addiction behaviors, where past consumption of addictive goods such as cigarettes increases the desire for present consumption. In our specification, we include the past consumption stock in the satiation parameter. A positive value for δ2i indicates that past consumption of cigarette brand j increases the marginal utility of consuming an additional package of brand j.In each period, a smoker chooses an optimal set of cigarette purchase quantities x over the J brands, which solves the following Lagrangian condition. We drop subscripts i and t in the following equation:L=ϕ1lnx1+∑j=28γjϕj{ln(xjγj+1)}−λ(∑j=18xjPj−E),4where λ is the Lagrangian multiplier associated with the budget constraint E, and Pj is the tax-inclusive cigarette price per pack of brand j. The Khun–Tucker first-order conditions for optimal purchase quantities xj* can be derived as follows:∊j=V1−Vj+∊1 if xj*>05∊j 1) or decrease (HR <1) in postpurchase hazard associated with each attribute.According to the results presented in Table 3, we find that the coefficient of the variable capturing the relationship between geographical distance and WOM effectiveness is negative and statistically significant (Visible message × Geographical distance:.945, p < .01, Model 5); all reported coefficients correspond to hazard ratios (HRs) representing the increase (HR > 1) or decrease (HR < 1) in purchase likelihood associated with each attribute. (Table A2 in the Web Appendix also shows the coefficients of the control variables.) Beyond the effect of interest, we also find a negative and statistically significant spatial homophily–based effect of geographical distance (Geographical distance:.954) as well as a positive and statistically significant effect of similarity (homophily). Interestingly, these findings show that despite the ""death of distance"" postulated in the literature ([28]), geographical distance is negatively associated with the effectiveness of eWOM. Thus, our research is the first to establish that geographical distance has a negative relationship with eWOM outcomes even among familiar social media peers, in addition to the previously known homophily-based effect of geographical distance.Moreover, the coefficients of all the other variables are in accordance with what one would expect as well as the extant literature on WOM (e.g., [13]; [20]; [45]). Specifically, we find that the increased user similarity and strength of relationship between users (User similarity: 1.264; Reciprocal relationship: 4.527) as well as more intense WOM advocacy (Sentiment of message: 1.556; Personalized message: 1.041) are associated with higher levels of purchase likelihood after exposure to WOM (Visible message: 1.608). Similarly, users with higher product expertise (User expertise: 1.191) seem more persuasive; thus, their followers are associated with a higher purchase likelihood after being exposed to their advocacy. Economic Significance and Managerial RelevanceThe relationship of interest is also of economic significance, as a decrease of 10 miles in the distance between users accentuates the effectiveness of eWOM by 12.78% based on the aforementioned coefficients; similarly, an increase of 100 miles in the distance corresponds to a decrease of 25.56%, and an increase of 1,000 miles corresponds to a decrease of 38.34%. For instance, for a recipient living in New York City, the relationship is reduced by 24.39% when the eWOM message originates from a sender in Philadelphia, and 38.82% from Miami, compared with the same message from a sender in New York City.We further assess the economic significance of the findings by measuring the out-of-sample performance of the models. Specifically, we use a holdout evaluation scheme with an 80/20 random split of data and evaluate the models in terms of Harrell's C concordance coefficient, which measures the likelihood of correctly ordering survival times for pairs of senders and recipients of eWOM messages; the concordance measure is similar to the Mann–Whitney–Wilcoxon test statistic as well as the area under the receiver operating characteristic curve. The results show that our model achieves a predictive performance of.840. Thus, it outperforms the baseline by a large margin, as the baseline performance corresponds to a value of.5. This statistically significant difference further illustrates the managerial relevance of the findings, as they can enhance seeding and targeting strategies ([57]; [99]).We further quantify the (dollar) value of this increase in out-of-sample performance ([89]). To conduct this calculation ([89]), we use estimates of the cost of targeting (e.g., promoting eWOM messages) and the average product price (Goldfarb and Tucker 2011); the cost of this type of targeting on Twitter is estimated to be $1.35 based on [111], while the average product price in our data set is $125. Combining these data reveals that our model suggests a profit of $.85 per targeted user, which corresponds to a 9% increase over the baseline of not using the information of geographical distance (i.e., $.78), while for random targeting the corresponding profit is only $.008. Potential MechanismOur findings are surprising, as in such an empirical setting the products are not location-specific, there are no transportation fees for consumers, and there is no contracting or potential conflict or ambiguity between senders and recipients of WOM messages ([59]; [79]). Thus, to fully understand our findings, we delve into a likely underlying mechanism of the identified effect and conduct additional analyses that allow us to assess the likelihood of this potential mechanism.We hypothesize that the negative relationship between geographical distance and eWOM effectiveness could be due to the identification processes of social media users. Specifically, a user who resides near the sender of the message is likely to share a common social identity with the sender based on their geographic proximity ([44]; [62]; [83]; [107]) and thus might be more susceptible to WOM influence originating from this (local) sender.[10] Conversely, a recipient who resides farther away from the disseminator of the WOM message is not likely to share the same location-based social identity and thus is less likely to be persuaded ([37]; [38]; [96]) by mere exposure to eWOM advocacy. Location-based social identity activationTo empirically assess this potential underlying mechanism, we first examine the moderating effect of the salience of the geographical distance from the source of WOM. Salience is activating common social identity identification ([44]; [55]; [56]; [68]; [77]; [106]), and thus, we empirically test the likely underlying mechanism by examining the moderating effect of the salience of the sender's location on the impact of geographical distance on the effectiveness of WOM. If the relationship is accentuated when the geographic proximity of the source of the WOM message is more salient, this would provide empirical support for the hypothesized mechanism of common social identity, as the salience of the location—and thus the salience of the geographic proximity—enhances the social identification processes of the recipient ([44]; [55]; [56]; [68]; [77]; [106]). This would also provide additional empirical evidence in favor of the main identification strategy. Alternatively, if more salient location cues attenuate the relationship, this would provide evidence against the hypothesized mechanism.According to the results presented in Table 4, we find a negative and significant moderating effect of the salience of the sender's location on the impact of geographical distance on the effectiveness of WOM; the salience of location variable corresponds to whether the location of the WOM sender is explicitly mentioned in her profile. That is, the relationship between geographic distance and eWOM effectiveness is even more negative when the distance is more salient. This finding indicates that common social identity is a likely mechanism for the identified relationship. The results are robust to alternative econometric specifications.[11]GraphTable 4. Estimation Results of eWOM Effectiveness Model with the Moderating Effect of the Salience of Geographical Distance. 7 *p < .1.8 **p < .05.9 ***p < .01.10 Notes: eWOM effectiveness analysis with the moderating effect of salience of geographical distance. The salience of location variable corresponds to whether the location of the disseminator is explicitly mentioned in the profile of the disseminator. Additional table notes as in Table 3. Location-based social identity prominenceWe also examine the likelihood of the hypothesized mechanism in additional ways. For instance, we examine the effectiveness of eWOM under conditions that strengthen the role of geographical location in the social identification process. Specifically, increased political homogeneity in the local area of the recipient of the eWOM message is likely to enhance the importance of the location-based social identity of the recipient as it increases the salience and significance of individuals' social identity due to political entities operating at geographic levels (e.g., precinct, county, state) and the characteristics of the local information environment (e.g., increased number of times an individual is reminded of the local identity, positive perceptions of the local community) ([62]; [91]; [101]). As a result, a pronounced location-based social identity of the recipient is likely to engender biases based on geographical distance, accentuating the relationship. Thus, if the relationship is accentuated when the political homogeneity in the local area of the recipient of the WOM increases, this would provide empirical support for the potential mechanism of social identification. Conversely, the opposite would provide empirical support against this potential mechanism.Based on the results in Table A3 in the Web Appendix, we find a negative and significant moderating role of the political homogeneity in the local area of the recipient; we have collected data from the MIT Election Lab (https://electionlab.mit.edu) on political voting patterns at the precinct level for the 2016 elections and measure political homogeneity on the basis of the percentage of voters that would need to switch from the majority party to the minority party for the two parties to have equal votes. Put simply, the negative relationship between geographical distance and eWOM effectiveness is amplified when location-based social identity might be more pronounced due to increased political homogeneity. This finding lends support to the hypothesized mechanism of social identity. The results are also robust to alternative specifications.Similarly, we also examine the moderating role of exogenous hardships in the local area of the recipient of the WOM message. If there have been significant local community hardships or natural disasters, then geography-based common social identity is likely to be more prominent for the residents of the affected area ([109]). Thus, if the relationship is accentuated when the geographic proximity of the source of the WOM message is combined with local community hardships for the recipient, this would provide additional support for the potential mechanism of common identity; we measure local community hardships using (exogenous) deaths related to extreme weather events in the location of the recipient of the message during the last five years prior to our observation window based on data from the National Oceanic and Atmospheric Administration. According to the results in Web Appendix Table A4, we find that the relationship between geographical distance and the effectiveness of eWOM is even more negative for users for whom location-based social identity is likely more pronounced due to local community hardships, lending empirical support to the hypothesized potential mechanism.[12] Location-based social identity versus other peer effectsWe also examine whether the estimated moderating role of geographical distance is above and beyond other potential peer effects. Table A5 in the Web Appendix presents the corresponding results controlling for both the interaction between user similarity and visible message and the interaction between sociodemographic distance and visible message. Our findings remain highly robust, further illustrating that the estimated moderating effect of geographical distance is above and beyond other peer effects, including actual and perceived homophily. The results are also robust to alternative specifications, such as including additional interactions. Ruling Out Additional Alternative ExplanationsIn addition to the aforementioned evidence and identification strategies, we assess various alternative explanations. Table 5 presents an overview of these, with the main ones discussed next and additional ones discussed in Web Appendix B.GraphTable 5. Alternative Explanations Local marketing promotion effectsOne may be concerned that the results might be driven by unpaid or organic marketing effects in the local region of the eWOM message recipient. To evaluate this, we supplement our data set with local web search trends for each product from Google Trends ([14]). Table A6 in the Web Appendix presents the corresponding results controlling for both local marketing expenditures and ad response (via search behaviors) of the local audience. The results remain robust, alleviating concerns that local marketing promotion activities drive the results. The results are also robust to alternative specifications, such as using national Google Trends and advertising expenditures or estimating separate models for each potential confound. Local user-preferences effectsWe also examine the robustness of the findings to alternative specifications, such as controlling for homophily based on the overlap in brands that each social media user follows on the platform. Table A7 in the Web Appendix presents the corresponding results. The results remain robust, further corroborating our findings. Small-city effectsAnother potential alternative explanation is that the results are driven by disseminator–recipient pairs located in small and remote locations as in such locations distances are in general shorter and demand for products sold online is higher due to the limited availability of other product alternatives ([21]; [79]). We assess this alternative mechanism by repeating the analysis excluding any observations that correspond to small and remote locations, as determined by the Census (i.e., locale assignments). Table A8 in the Web Appendix presents the results; the results remain robust. Local weather conditions effectsWe also assess the alternative explanation that the results are driven by the local weather conditions affecting the moods and activities of users ([46]; [67]). We assess this potential explanation by controlling for the temperature, precipitation, and sunshine levels in the location of the recipient using data from the National Oceanic and Atmospheric Administration. Web Appendix Table A9 presents the corresponding results; the results remain robust. Robustness Checks and Alternative Identification StrategiesWe also undertake an extensive set of tests to assess the robustness of the results and further strengthen the findings, as discussed next; see Table 5 and Web Appendix B for additional details. Extended econometric specificationsFirst, to enhance the employed identification strategy and examine the robustness of the findings, we further control for latent user characteristics by tapping into the social network structure and recent deep-learning advances. Specifically, we use the method of DeepWalk, a deep-learning method for graphs ([88]), to learn the latent representations of the users and their similarity and further account for both network structure roles and latent user homophily. Table A10 in the Web Appendix presents the corresponding results. The results corroborate our findings. The results also remain robust to employing alternative deep-learning methods, such as the node2vec method ([53]).We also repeat the analysis including multiple user similarity measures. In particular, the similarity measures correspond to the similarity levels between disseminators and recipients based on ( 1) the Jaccard coefficient of their followers, ( 2) the Jaccard coefficient of their followees, ( 3) the topics discussed in social media posts using the results of the LDA model, ( 4) the intrinsic brand and product preferences of the users based on the overlap in brands that each social media user follows on the platform, ( 5) the demographic information at the corresponding geographic locations (i.e., average age and percentages of male, Black or African American, Hispanic, and Asian-origin residents based on Census data), and ( 6) the latent characteristics of the users based on the deep-learning methods for representation learning; in addition to ( 7) the reciprocity of the relationship and ( 8) the number of interactions between the users. Table A11 in the Web Appendix presents the corresponding results; the results remain robust. Alternative identification strategiesWe also examine additional alternative identification strategies to control for any potentially remaining differences between the visible and nonvisible messages; Table A1 in the Web Appendix presents a summary of the different identification strategies. First, we enhance our identification strategy following the covariate adjustment method of [60]. Table A12 in the Web Appendix presents the corresponding results. The results remain robust; the results are also robust to including additional covariate interactions.Moreover, as an alternative identification strategy, we combine propensity-score matching with the main research design. In particular, we model the propensity of each message to be rendered visible using all the variables that describe the users' relationship and the message characteristics as well as the geographical distance between the users.[13] We conduct the matching based on the propensity scores before estimating again the same econometric models (for additional details, refer to the corresponding table notes). For this robustness check, we use one-to-one matching with replacement and a caliper of.05, yielding a standardized mean (median) absolute difference of.009 (.007) across all the variables, which ensures that covariate balance has been successfully achieved ([18]); the density distributions of the propensity scores also indicate significant overlap and common support. As shown in Table A13 in the Web Appendix, the results remain robust. The results are also robust to nearest-neighbor matching with the generalized Mahalanobis distance.Furthermore, as an additional alternative identification strategy, we build latent variable models where the sender–recipient similarity is latent and measured based on the various similarity features. Web Appendix Table A14 shows the corresponding results. Model 1 corresponds to the aforementioned latent variable model, while Model 2 combines the latent variable model with propensity-score matching estimating the model over the matched sample. The results of all the aforementioned alternative models are highly consistent and further corroborate our findings.Finally, as an alternative strategy, to estimate the relationship between geographical distance and eWOM effectiveness, we also use a logit model ([105]) examining whether—rather than how quickly—a user purchases a product. As Web Appendix Table A15 shows, the results remain robust. Falsification testsWe supplement these robustness checks with falsification tests to further assess whether the previous models are picking up spurious effects. As shown in Web Appendix Table A16, the results indicate our findings are not a statistical artifact of the specifications.Overall, the findings remain highly robust to various alternative identification strategies, econometric specifications, robustness checks, and falsification tests. Figure 3 illustrates the corresponding estimated effects across the specifications.Graph: Figure 3. Hazard ratios (HRs) with 95% confidence intervals (whiskers) representing the percentage increase (HR > 1) or decrease (HR < 1) in postpurchase hazard across estimated models. Discussion and ImplicationsIn this study, we investigate the relationship between geographical distance and the effectiveness of eWOM. Specifically, we examine whether the geographical distance between familiar disseminators and receivers of eWOM messages plays an important role—beyond utilitarian reasons and proxying for consumer tastes—in driving recipients' subsequent purchase behaviors. Our results show that the relationship between eWOM and the likelihood that message recipients subsequently also make product purchases significantly strengthens as the spatial proximity between disseminators and receivers grows. Implications for TheoryOur findings help advance understanding of conditions that affect online WOM performance. Many of the characteristics previously shown to impact eWOM outcomes relate to the product, brand, or message ([73]; [85]). We contribute by illustrating the role of the important but often overlooked construct of geographical distance in eWOM effectiveness. In showing how geographical distance is still associated with the effectiveness of online WOM in the absence of geography-specific transaction costs between unambiguous users, we demonstrate how the social force field of geography can tether the potential of eWOM. That is, despite the promise of technology to reduce communication barriers and the proclaimed ""death of distance"" ([29]; [52]), we find that geographic constraints persist online in unexpected ways. Therefore, our results also help address the debate on whether and how geographical distance still matters online ([51]) by showing that it can shape the influence of eWOM.We also contribute to the theory of eWOM examining why geographical distance is associated with eWOM effectiveness. Specifically, we find evidence that social identification may explain why the influence of online WOM is negatively related to the distance between WOM message disseminators and receivers. That is, our results suggest consumers are susceptible to online information and cues related to social identification as they can, in turn, enhance message persuasiveness. Thus, information and cues relating to social identity can be agents of eWOM influence. Whereas much of the literature on the role of geographic distance in e-commerce and other online settings offers economically driven explanations for the impact of geography, our study proposes behavioral bias relating to social identification may be an underlying mechanism that drives the relationship between geographic distance and eWOM. This finding highlights the need for future research to study additional non–economically driven explanations that can induce such biases. Implications for PracticeOur findings have important implications for managers as well. For instance, a controversial argument in the industry is that solely characteristics of the disseminators catalyze the adoption of behaviors and products and thus much of marketing efforts to engineer WOM in social media focus on identifying such characteristics. However, our findings indicate that marketers should expand their focus to take into account the disseminator–recipient pairings and understand that factors pertaining to these pairs can be significantly related to the effectiveness of eWOM. In particular, our results suggest geographical distance matters in online WOM and,, thus marketers can readily take advantage of how geographical distance is associated with eWOM persuasion. Marketers may thus adopt data-driven strategies to selectively promote eWOM episodes according to the proximity of such episodes to each consumer, or to strategically engineer such episodes based on geolocation information. Interestingly, although research has begun to identify pairwise characteristics between senders and receivers that shape the influence of eWOM, such as tie strength and similarity across personality traits ([ 1]; [20]), many of these factors are not readily observable to managers who wish to capitalize on them. The distance between social media users, though, is more easily observable to managers.Beyond promoting or engineering geographically proximate eWOM episodes, marketers may also benefit from promoting and/or engineering episodes containing other social identity cues. The likely connection between eWOM outcomes and social identity suggests that firms may also consider other cues relevant to social identity formation to further boost the success of interpersonal communications and WOM messages, as enhancing social identification may significantly increase message persuasion and user engagement in the online world.Our findings also have important implications for the effective design of viral marketing campaigns and ad content. Specifically, brands may boost the persuasiveness of their marketing campaigns by infusing into their content local cues or other identification triggers to induce consumers' social identification processes. Relatedly, marketers are beginning to leverage users' connections on social networks to develop and deliver marketing communications as part of their social advertising efforts. Our research suggests that they could further improve the effectiveness of these strategies by selecting geographically proximate connections to their targets.Furthermore, going beyond advertising strategies, the implications of our work also provide actionable guidelines for optimizing the delivery of digital content. In particular, our findings can help platforms increase the effectiveness of their content curation and ranking algorithms by incorporating information on content location or source origin and by factoring geolocation into their determination of which user-generated content to disseminate. For instance, content generated by spatially proximate consumers may draw more attention due to identification processes and thereby increase the effectiveness of content provision. In a similar vein, social media platforms may also consider incorporating location information in other functions. For instance, social media platforms may incorporate such information into various other machine-learning algorithms, such as their whom-to-follow recommendations. In addition, our findings could also be used by marketers and platforms to better predict the diffusion of information, products, and user behaviors in social media ([ 5]).Lastly, deepening our understanding of the factors that can attenuate or accentuate the effectiveness of eWOM has important implications that extend to public policies. For instance, revealing how geographic proximity is positively associated with eWOM effectiveness is critical for the development of effective public policies to induce positive behavioral changes, such as voter turnout, civic engagement, and public health actions. Limitations and Future ResearchWhile our work makes important strides in understanding how geographic proximity is related to eWOM effectiveness, we acknowledge certain limitations, which mostly stem from data availability issues. For instance, we examine the relationship between geographical distance and eWOM in a single social media platform because the service provider launched this venture on only one platform. Future research could examine whether the observed relationship manifests differently on other platforms. Moreover, we did not manipulate the visibility of the messages on Twitter because the venture did not alter the functionality of the platform in any way; future research could consider directly manipulating the visibility of the messages. Similarly, we did not manipulate the geographical distance of users from their followers. In addition, while we capture actual purchases in our data, we do not capture other consumer behaviors that could indicate interest in the products, such as online searches, as this type of information was not available to us. It would be interesting for future research to further examine such potential effects. Future research could also further examine and validate the underlying mechanisms. While we provide evidence that social identification may account for the relationship, future work may conduct experiments to verify this. Lastly, we do not observe in our data private communications between individuals due to privacy reasons and ethical concerns. Nevertheless, we hope these limitations provide avenues for future research that can deepen understanding of the critical role geographic proximity plays in eWOM and other online settings. " 36,"Leapfrogging, Cannibalization, and Survival During Disruptive Technological Change: The Critical Role of Rate of Disengagement"," When faced with new technologies, the incumbents' dilemma is whether to embrace the new technology, stick with their old technology, or invest in both. The entrants' dilemma is whether to target a niche and avoid incumbent reaction or target the mass market and incur the incumbent's wrath. The solution is knowing to what extent the new technology cannibalizes the old one or whether both technologies may exist in tandem. The authors develop a generalized model of the diffusion of successive technologies, which allows for the rate of disengagement from the old technology to differ from the rate of adoption of the new. A low rate of disengagement indicates people hold both technologies (coexistence), whereas a high rate of disengagement indicates they let go of the old technology in favor of the new (cannibalization). The authors test the validity of the model using a simulation of individual-level data. They apply the model to 660 technology pairs and triplets–country combinations from 108 countries spanning 70 years. Data include both penetration and sales plus important case studies. The model helps managers estimate evolving proportions of segments that play different roles in the competition between technologies and predict technological leapfrogging, cannibalization, and coexistence.","In July 2020, Tesla became the world's most valuable automaker, surpassing Toyota in market value for the first time ([30]). But it was Toyota that in 1997 released the Prius, the world's first mass-produced hybrid electric vehicle. In 2006, Tesla Motors, an upstart entrant, bet that the future of the automotive industry would be fully electric cars. They announced they would produce luxury electric sports cars that could go more than 200 miles on a single charge. Incumbents dismissed the effort as futile because of the high entry barriers for auto production, the high cost of producing in California, and the challenges of establishing charging stations. But Martin Eberhard, Tesla's cofounder, noted in a blog in 2006, ""a world of 100% hybrids is still 100% addicted to oil...Tesla Motors will remain focused on building the best electric cars for the foreseeable future. With each passing year, our driving range will get longer and the argument for plug-in hybrids will get weaker. To hell with gasoline"" ([ 8]).In contrast, Toyota bet that hybrids would be the future. ""The current capabilities of electric vehicles do not meet society's needs, whether it may be the distance the cars can run, or the costs, or how it takes a long time to charge,"" said Takeshi Uchiyamada, Toyota's vice chairman, who had spearheaded the Prius hybrid in the 1990s ([18]). Toyota faced a hard choice: invest in hybrids, all-electrics, or both?Globally, during times of potentially disruptive technological change, both industry incumbents and new entrants face difficult choices. For incumbents, the critical dilemma is whether to cannibalize their own successful offerings and introduce the new (successive) technology, survive with their old offerings, or invest in both. To address this dilemma, they need to know whether disruption is inevitable; that is, will the old technology sales decline due to the growth of the new technology and, if so, how much of their existing sales will be cannibalized over time? Or can both old and new technologies, in fact, coexist in tandem? The entrant's dilemma is whether to target a niche and avoid incumbent reaction or target the mass market and incur the wrath of the incumbent ([38]). To address these dilemmas, both incumbents and new entrants need to know how segments of consumers respond to the successive technology. Examples of technological change abound: electric cars versus hybrid cars versus gasoline cars, OLED TVs versus LCD TVs, streaming versus cable, music downloads versus CDs, laptops versus tablets, and app-enabled ridesharing versus taxicabs. Several incumbent firms have also stumbled or failed during disruptive change: Toyota, GM, HP, Nikon, Canon, Kodak, Sony, Nokia, Yellow Cabs, Comcast, and Sears.Our central thesis in this article is that to effectively manage disruption, we must answer the following substantive research questions: First, when does an old technology coexist with a new, successive technology versus going into an immediate decline? If coexistence occurs, how can one account for the coexistence of two technologies in an empirical model? Second, how can one estimate the extent of cannibalization and leapfrogging of an old technology by a new technology over time? Third, can consumer segments explain coexistence, cannibalization, and leapfrogging in successive technologies, and if so, which segments?These questions represent pressing concerns for senior managers ([21]). To address these questions, we first outline the theory of disruption, discuss research gaps, and define important constructs that are central to the new model and typology. Then, we develop a generalized model of the diffusion of successive technologies. A key feature of the generalized model is the rate of disengagement from the old technology, which is not forced to equal the rate of adoption of the successive technology, allowing both technologies to coexist. Next, we estimate four latent adopter segments from aggregate data, which correlate with the growth of the new technology, the cannibalization of the old, and/or the coexistence of both: leapfroggers, switchers, opportunists, and dual users (defined shortly).We apply our model to three different types of aggregate data to ascertain model fit: ( 1) penetration of seven successive technology pairs across 105 countries (441 technology pair–country combinations) spanning multiple years, ( 2) sales of three contemporaneous technology pairs across 40 countries (92 technology pair–country combinations), and ( 3) case analyses of real disruption of large incumbents in the United States. The major benefit of using aggregate penetration and/or sales data is that such data are available abundantly compared to individual-level data. Indeed, much research uses this type of aggregate data to generate rich insights on adoption, diffusion, and generational competition (see [ 4]; [ 5]; [17]; [37]). In addition, we present a test validating the model using a simulation analysis on individual consumer-level data.Our model and analysis provide both substantive and modeling innovations. Our research provides a better strategic understanding of how, in many situations, old technologies may not necessarily die but survive when new, successive technologies are introduced. The major contributions and implications are the following: First, disruption, though frequent, is not inevitable even when the successive technology grows rapidly, as old technologies can coexist as partial substitutes of the new. Second, the generalized model of diffusion of successive technologies helps strategists and marketers account for this coexistence by allowing the rate of disengagement from the old technology to differ from the rate of adoption of the new. Third, the separately estimated rate of disengagement enables a superior fit to data on technological succession. Fourth, the model helps estimate cannibalization by new, successive technologies, as well as sizes of four critical segments, providing key signals about disruption. The coexistence of both technologies occurs when there is a large segment of dual users. In contrast, the size of the leapfroggers segment correlates with the growth of the new technology, and the size of switchers and opportunists correlates with cannibalization of the old technology. Fifth, the profit implications of leapfrogging and cannibalization may vary greatly depending on which firms market which technology. Major incumbents may fail during the takeoff of new technologies due to underestimating the size of leapfroggers (opportunity cost) and switchers (real cost). Sixth, the generalized model can capture variations in segment sizes across technologies and global markets. The next sections present the theory, new typology, model, empirical applications, and strategic implications. TheoryThe theory of disruptive change ([ 2]; [ 6]) suggests that a new technology enters a market, improves in performance, and then surpasses the performance of the existing technology. During times of such technological change, leading incumbent firms fail, not because they were technologically incapable of producing and marketing these innovations themselves, but because they focus on their existing (mainstream) customers, who were satisfied with the existing technology because it met their needs on the primary dimension of performance ([ 6]).Christensen and his coauthors suggest that the new technology enters, survives, and grows because it offers benefits on a secondary dimension of performance that appeals to niche segment consumers. Over time, the new technology improves in performance and at some point meets the standards of the mainstream segment on the primary dimension of performance. These customers then switch to the new technology. Disruption occurs if the incumbent focuses on the old technology to the exclusion of the new one.Several authors have criticized the theory of disruption because of circular definitions, lack of large empirical evidence or a predictive model, and a failure to examine whether consumer behavior changes (e.g., [25]; [35]; [36]; [34]). However, no study has refuted the essential features of the theory of disruption: that successive technologies do compete, the competing technologies appeal to different segments, the new technology grows in performance over time, and the niche it serves grows in response to this improvement.A major limitation of prior work on disruption is that it does not provide recommendations on some critical issues that concern both incumbents and new entrants: How can they estimate the extent of cannibalization over time and who are the customers most susceptible to the new technology? Could the two technologies coexist, and which segments drive the coexistence of both technologies and the growth of the new technology? This research seeks to address these issues. Definitions and a Typology of New Adopter Segments for Successive TechnologiesTo answer the previous questions using the theory of disruption, we define the concepts of successive technology, substitution, and segments. Successive technologyA new successive technology (which can include both a technology and a product) addresses similar underlying consumer needs as the old technology (e.g., DVR vs. VCR) or may tap simultaneously into multiple needs (e.g., PC, laptop, tablet). Successive technologies do not include new generations of the same product. Note that in this article, we use the term ""successive technology"" synonymously with ""new technology"" and the term ""old technology"" synonymously with ""prior technology,"" given the context of technological succession. ""Cannibalization"" is the extent to which the successive technology ""eats"" into real or potential sales (or penetration) of the old technology due to substitution. Rate of disengagement (F 12)Much research in marketing (e.g., [ 7]; [13]; [24]; [26]; Table 1) addresses the related issue of the diffusion of perfectly substitutable successive generations of the same technology (e.g., iPhone 8 vs. iPhone 7), in which the consumer always prefers the new generation to the old at the same price (e.g., iPhone 9 and 10). Thus, successive generations of the same technology exhibit perfect substitution. Here, consumers completely disengage from the old generation (of the same product) when they adopt the new one.GraphTable 1. A Comparison with Related Literature on Generational Substitution. Technological competition is more complex than intergenerational competition because successive technologies may be only partial substitutes. That is, whereas some consumers prefer the successive technology over the old technology (e.g., teens), other consumers may find value in and prefer to hold both (e.g., homeowners who have PCs, laptops, and tablets or keep both mobile phones and landlines). For example, while the two technologies may differ in terms of the scientific principle, the old technology may still serve a need that the successive technology cannot fulfill. In such a case, a group of adopters could choose to hold both technologies, triggering the need for a model that does not force complete substitution. In this case, consumers do not fully disengage from the old technology and may co-own successive technologies.For example, consider Figure 1a, which plots the penetration of VCRs and the successive technology of DVD players. Here we observe a fast adoption of DVD players, but over this same period, the decline in VCRs (Technology 1) is relatively small. In other words, a number of customers initially held on to both technologies before switching entirely to DVD players. Figure 1a also shows other such examples of the coexistence of successive technologies. Figure 1b shows a similar initial coexistence in sales of technology pairs. Therefore, to model the diffusion of successive technologies, one needs to allow for a rate of disengagement from the preceding technology that is not exactly equal to the rate of adoption of the new technology (i.e., one must allow for partial substitution). This inclusion of a separate rate of disengagement (F12 in this article) is one of the innovations we propose in this research. A low rate of disengagement indicates consumers hold on to both technologies, whereas a high rate indicates they discard the old technology in favor of the new. Thus, the greater the rate of disengagement, the greater the cannibalization of the old technology by the new technology.[ 5]Graph: Figure 1a. Market penetration of select technology pairs.Graph: Figure 1b. Sales of select technology pairs. Adopter segments for a new successive technologyWe define and derive mathematically a typology of four adopter segments for successive technologies: ( 1) ""Leapfroggers"" adopt the successive technology but would never have adopted the old technology and thus present a new consumer segment for the new technology. This is the niche in Christensen's theory of disruption that provides initial sales for the new technology. ( 2) ""Switchers"" are consumers who had already adopted the older technology but who choose to replace it with the successive technology after the latter technology is introduced. In Christensen's theory of disruption, this is the mainstream consumer segment that switches to the successive technology after it improves. The refinement in our empirics is that this segment switches continuously to the successive technology as it improves. Each year, customers switch as the successive technology matches their needs better than the old technology. ( 3) ""Opportunists"" are those who would have adopted the old technology but delayed the decision and instead end up adopting the successive technology. ( 4) ""Dual users"" are those who had already adopted the older technology but who elect to adopt/use both technologies once the successive technology is introduced. This segment also includes those who would have adopted the old technology but had delayed the decision and ended up adopting and using both technologies. A Generalized Model of the Diffusion of Successive TechnologiesMany situations exist in which one technology substitutes for another but the substitution is only partial, either due to incomplete compatibility or because the old technology still has its uses. Thus, it makes sense to hang on to the old technology because it is still useful (e.g., VHS vs. DVD), even in the presence of the new. Currently, no model allows for this coexistence of successive technologies. However, multigenerational models such as [26] and [14] model the diffusion of successive generations of the same technology. Although the [26] model is not right for multitechnology substitution, a modification of the Norton–Bass model is well-suited for this context.Our proposed model uses the multigenerational model of [26] as a starting point and extends this model to consider the context of the adoption of successive technologies that do not fully cannibalize each other (partial substitution). The major difference in our model is that we include a rate of disengagement from the old technology that does not equal the rate of adoption of the successive technology, which accounts for partial substitution in the case of successive technologies versus complete substitution in the case of successive generations of the same technology.Herein, we ( 1) specify our intuition that motivates the derivation of adopter segments for successive technologies, ( 2) outline our model for the diffusion of two successive technologies (the Web Appendix provides an extension to multiple technologies), ( 3) discuss our critical departure from the basic model of multigenerational diffusion (i.e., we provide a more flexible model in which we do not force the rate of disengagement from Technology 1 [this term is used in this section to concisely reflect the old technology] to exactly match the rate of adoption of Technology 2 [we use this term for the successive technology]), and ( 4) illustrate the equations we used to decompose adoption into four adopter segments. The Model for Diffusion of Two Successive TechnologiesWe specify the proposed model for the simplest case of the diffusion over time of two successive technologies as follows. Let S1(t) and  S2(t)  respectively represent the penetration of Technologies 1 and 2 at each time period  t . Then we model S1(t) and  S2(t) as follows: S1(t) = m1F1(t)(1 − F12(t − τ2 + 1)) Graph1  S2(t) = F2(t − τ2 + 1)(m2 + m1F1(t)) Graph2Note we have added the 1 in Equations 1 and 2 to account for the fact that we are only considering whole years. τ2 corresponds to the introduction year for Technology 2,   and Fg(t) = pg(1 − e−(pg + qg)t)pg + qge−(pg + qg)t, t ≥ 0, g = 1, 2, or 12 Graph3refers to the fraction of all potential Technologyg consumers for each technology at time t. Here, g refers to a technology (rather than a generation of a technology as is typically considered in the literature on multigenerational diffusion). Our model contains eight parameters: m1, m2, p1, p2, p12, q1, q2, and  q12 . The parameter  m1 represents the long-run penetration for Technology 1 if Technology 2 had never been introduced. Put another way, prior to the introduction of Technology 2, the penetration for Technology 1 will converge toward m1 but will never reach m1  because for t ≥ τ2, Technology 2 will start to reduce the market share of Technology 1. Thus, Technology 2 begins to take market share from Technology 1 upon its introduction. Similarly, m2 represents the additional market share for Technology 2 above that of Technology 1, so our model assumes that the long-run penetration for Technology 2 will equal m1 + m2 . The parameters p1 and p2 are the coefficients of innovation for Technologies 1 and 2, respectively, and q1 and q2 are the coefficients of imitation for Technologies 1 and 2, respectively. p12 and q12 can then be thought of as the coefficients of disengagement. Thus, F1 describes the rate at which customers adopt Technology 1 prior to the introduction of Technology 2, and F2 models the rate of adoption of Technology 2 after its introduction. Finally, F12 models the rate at which Technology 1 customers disengage upon the introduction of Technology 2.Note that we make two critical departures in this specification from what is typical of multigenerational diffusion models. Typically, multigenerational diffusion models restrict F2 to equal F12 . The proposed model removes such a restriction for the context of successive technologies. The potential advantage of modeling F2 and F12 separately is as follows: when F2 = F12,  the rate of disengagement by current Technology 1 customers exactly matches the rate of adoption by Technology 2 customers. However, in the case of successive technologies, across categories and countries, consumers may in fact hold both technologies simultaneously. For example, many families with older members have both a landline and a mobile phone. In addition, both technologies may grow simultaneously in different customer segments. Therefore, one of our innovations in developing a corresponding model to fit the context of successive technologies is to allow F12 to be less than F2 , which corresponds to people adopting Technology 2 at a faster rate than they leave Technology 1. If F12 = 0 , then there is no substitution effect and people are holding on to both technologies. When F12 is large, there is a large substitution effect. This is a strength of the model because we can directly measure the substitution effect rather than forcing F2 to equal F12 .Second, an important distinction from prior models is that we also do not constrain p1 to equal p2 or q1 to equal q2 , a constraint that is suitable when the changes between the two generations are incremental, as in multigenerational diffusion, but not when the technology is discontinuous ([24]), as in our more general case of successive technologies. Given that each successive new technology provides a substantial improvement in benefits, we expect the diffusion parameters p and q to vary for each new technology in a pair or triplet. Thus, our model does not constrain p1 to equal p2 or q1 to equal q2 .Note that, similar to previous models, we make certain assumptions. First, we assume a pure Bass model formulation for the first technology ([ 1]). However, we acknowledge that the first technology may have been affected by a previous technology. Second, we model F12 using the same functional form as F1 and F2 for two reasons. Empirically, we find that the model with this form fits our data well. In addition, by modeling F12 using the same functional form as F2 , our approach reduces to the standard [26] and [14] formulations whenever F12 = F2 . Thus, we provide a strict generalization of previous models. Overall, however, our model is a generalized model that can apply to both generational diffusion and technology diffusion. Model EstimationLet Sig represent the observed yearly penetration of Technology g at time ti . Then, estimating the eight parameters in Equations 1, 2, and 3 can be achieved using nonlinear least squares. In particular, we select m1, m2, p1, p2, p12,q1, q2, and q12 as the values that minimize ∑i = 1n(Si1 − m1F1(ti)(1 − F12(ti − τ2 + 1)))2+ ∑i = 1n(Si2 − F2(ti − τ2 + 1)(m2 + m1F1(ti)))2, Graph4where  n represents the number of years of observation. We minimize Equation 4 using the NLS function in the statistical software package R. Once the parameters have been estimated, it is a simple matter to plug the estimates back into Equations 1 and 2 to predict future penetration for Technologies 1 and 2. Computing Segments of Adopters for the New Successive TechnologyNext, we decompose penetration of Technology 2 into the four major segments defined earlier. Switchers (SW) and opportunists (O) represent a lost market for Technology 1 and thus its cannibalization (CAN), whereas leapfroggers (L) and dual users (DU) represent market growth (MG). Therefore, S2(t) comprises the sum of these segments as such: S2(t) = MG2(t) + CAN2(t) = L2(t) + DU2(t)︸Market growth   + SW2(t) + O2(t)︸Cannibalization . Graph5Similarly, S1(t) comprises the initial market for this technology ( L1 ) less cannibalization from Technology 2 as such: S1(t) = L1(t) − CAN2(t) = L1(t) − (SW2(t) + O2(t))︸Cannibalization . Graph6We derive the various consumer segments as follows: L1(t) = m1F1(t), L2(t) = m2F2(t − τ2 + 1) Graph7 SW2(t) = m1∑θ = τ2tF1(θ − 1)(F12(θ − τ2 + 1) − F12(θ − τ2)) Graph8 O2(t) = m1∑θ = τ2tF12(θ − τ2 + 1)(F1(θ) − F1(θ − 1)) Graph9  DU2(t) = m1F1(t)F˜2(t − τ2 + 1), Graph10where F˜2(t) = F2(t) − F12(t) .It is not hard to verify that the four quantities in Equations 7– 10 satisfy Equations 5 and 6. Let us first consider L2(t) . Recall that m2 represents the total potential additional market for Technology 2 beyond that of Technology 1 and F2 provides the fraction of potential customers who have actually adopted the new technology. Thus, L2(t) corresponds to the total number of additional Technology 2 adopters who would never have adopted Technology 1. Next, consider O2(t) . Note that m1(F1(θ) − F1(θ − 1))  represents the number of customers who would be expected to adopt Technology 1 in time period θ . However, F12 of these customers switch directly to Technology 2, while F˜2 = F2 − F12 customers adopt both technologies. Therefore, summing from τ2 up to t gives the total number of opportunists (Equation 9). DU2(t) corresponds to dual users who adopt both technologies. Here, m1F1(t) represents the number of people who have adopted Technology 1, and F˜2(t) represents the fraction of these people who have adopted both technologies.Finally, the switchers correspond to the remaining adopters of Technology 2, which can be shown to correspond to Equation 8. At θ = τ2  , this equation is fairly intuitive because m1F1(τ2 − 1) represents the current number of Technology 1 adopters and F12(t) represents the fraction of potential customers who drop Technology 1 to adopt Technology 2 in period θ = τ2 . Thus, Equation 8 assumes that current customers of Technology 1 switch to Technology 2 at the same rate as noncustomers of Technology 1. However, for θ > τ2 , the intuition becomes more complicated because the number of Technology 1 customers will be less than m1F1(t − 1) as a result of prior switching.Note that we have chosen to focus on identifying the adopters of the new technology. While we consider the role of dual users, who continue to find value in the old technology, we do not distinguish, for the sake of simplicity, between other types of old technology adopters—for example, those who may never adopt either technology, those who are yet to adopt the old technology but will not adopt the newer technology, and those who will stay loyal to the old technology.We can extend this model to more than two technologies. In markets characterized by excessive turbulence, a third technology is often introduced in quick succession to the second technology. We can extend our model to account for G ≥ 2 different technologies: S1(t), S2(t), ..., SG(t) . Here, successive technologies cannibalize the market of earlier technologies. In the interest of brevity, we detail the model extension to three technologies and its application for data on technology triplets in Web Appendix W1. Model BenefitsThe proposed model allows us to extract the sizes of the four adopter segments for each year and technology pair in each country using the defined equations. Our model has several additional desirable characteristics. First, the model parameters have natural interpretations. For example, Fg corresponds to the rate that individuals would adopt technology g in the absence of any competing technologies, and Fg − 1, g represents the rate that individuals disengage from Technology g − 1 to adopt Technology g . Second, by setting Fg − 1, g = Fg , our model reduces to that of [26] and [14], so their model can be seen as a special but more restrictive version of our approach for this context. Our empirical results suggest that our model provides a significantly more accurate fit to the data on successive technologies. Third, market growth generated by a particular technology can be easily computed as the sum of leapfroggers and dual users, and cannibalization can be computed as the sum of switchers and opportunists. Fourth, we do not place any restrictions on the size of adopter segments. Thus, market growth can be positive or negative. The latter case occurs when the total market size actually declines with the introduction of a new technology, possibly indicating disruption by yet another technology. While not the norm, our empirical results suggest that market growth can at times be negative when a still newer technology emerges for which we do not have data. Model Validation: Can the Model Recover Meaningful Structure from Individual Data?One may ask what evidence we have that our model can correctly recover individual consumer segments given that we have only aggregate data. To validate our model for this purpose, we ran a series of simulation analyses following precedents in model simulation ([28]; [39]). For our data generation process, we simulated the adoption of two technologies by a large group of individual customers. The simulation demonstrates a good fit with only ten years of data for Technology 1 (i.e., the model yields a reasonably good fit with only five years after Technology 2 enters the market) (Simulation Exercise 1). With more years of simulated data, the fits become even more accurate. Next, we show the robustness of the simulation analysis to the inclusion of a continuous heterogeneity distribution (Simulation Exercise 2) and the absence of some of the segments altogether (Simulation Exercise 3). These exercises provide more confidence that our model can uncover meaningful structure from the aggregate data even when the model assumptions do not hold exactly. Details are in Web Appendix W2. Empirical Applications of the ModelThis section covers applications of the model using data from different contexts. Analysis of Cross-Country Penetration of Technology PairsWe examined the fit of the model using the market penetration[ 6] of seven technology pairs (telephone–mobile phone, dial-up internet–broadband, black-and-white TV–color TV, VCR–DVD player/recorder, DVD player–Blu-ray player, personal computer–laptop, and laptop–tablet) spanning 105 countries (441 technology pair–country combinations). The data were compiled from several sources (Passport Euromonitor, Fast Facts Database, and the telecommunications database of the International Telecommunications Union). Model fitOverall, the proposed model fits the data well. Table 2 presents comparisons of the penetration data for four technology pairs using both mean-squared and median-squared errors of our proposed model with the separately estimated disengagement rate compared to the reduced form model using the simplifying assumption F2 = F12.  Our proposed model gets much smaller error rates than the latter model.GraphTable 2. In- and Out-of-Sample Fit Statistics for Technology Pairs Using Penetration Data. Table 2 presents the results by old and new technology as well as the average error across both technologies for the four pairs (the subsample is displayed for brevity). We derived the mean errors in the ""training,"" or in-sample data, by excluding the last time point for each curve, fitting each of the two competing models to the remaining time points, and calculating the mean of squared errors between the observed and predicted points for each technology pair across countries. In contrast, we derived the ""test,"" or out-of-sample results, by excluding the last time point from each curve and fitting the models to the remaining time points (K = 1). However, in this case, the mean squared error is calculated using the squared difference between the final year's observed and predicted points and calculating the overall average error across countries for each technology pair. Overall, our model fits much better out of sample as well as in sample, which is the true test for better performance of our model. The median error rate refers to the in-sample and out-of-sample error rate across the different countries—using the median instead of the mean—to account for the fact that some countries may greatly influence the averages.[ 7] See Figure 2 for some illustrative fit plots. Web Appendix W3 presents an analysis for K = 3 and 5 years. Overall, this analysis indicates that our model, which allows F12 < F2  , still outperforms a model that allows F12 = F2 . Table 3 provides the mean parameter estimates for these technology pairs.Graph: Figure 2. Sample fit plots from application of model with penetration data.Notes: Displayed are the fit plots for sample technology pairs. The black lines are the real data. The red line is plotted using our model (F2 ≠ F12) and the green dashed line is for the model with F2 = F12. The vertical lines represent the year of introduction of the new technology into the market.GraphTable 3. Parameter Definitions and Estimates. Our model allows us to decompose penetration for technology pairs into adopter segments. We provide an illustrative example for telephone–mobile phones in India. In Figure 3a, L1 is the projected penetration of Technology 1 (telephone) if the successive technology (mobile phone) were absent. S1 is the estimated penetration for Technology 1, indicating the effect of cannibalization (L1 − Cannibalization) due to switchers (SW) and opportunists (O). In Figure 3b, S2 (penetration for Technology 2 (mobile phone) is decomposed into leapfroggers (L2), total cannibalization (switchers (SW) + opportunists (O)), and dual users (DU). Here, the penetration of mobile phones is initially dominated by leapfroggers, followed by growth from cannibalization. In Figure 3c, S1 + S2 represents the evolution of the overall market due to market growth from Technology 2 (leapfroggers + dual users) compared to the presence of only Technology 1 (L1). Overall, the introduction of mobile phones in India created market expansion.Graph: Figure 3a. Decomposition of penetration of telephone (old technology) in India.Graph: Figure 3b. Decomposition of penetration of mobile phone (new technology) in India.Graph: Figure 3c. Evolution of the market (India telephone and mobile phone).Notes:Figure 3a shows the projected penetration L1 of Technology 1 if the successive technology were absent and the effect after cannibalization from Technology 2, represented by S1, the estimated penetration. Figure 3b shows the breakdown of the penetration curve (S2) for Technology 2 (mobile phone in India) into leapfroggers (L2), cannibalization (switchers [SW] + opportunists [O]), and dual users (DU). Figure 3c shows the evolution of the overall market (S1 + S2) due to market growth (MG) from Technology 2 (leapfroggers + dual users) compared to the market in the presence of only Technology 1 (L1). The figures are plotted over the lifetime of available data for Technology 1. Are Adopters of Successive Technologies Similar Across Categories?We next present some key results derived from decomposition of the data across the 441 technology pair–country combinations ten years from the commercialization of the new technology, using our model. Figure 4a presents the average size of the adopter segments across categories. Notice that for the transition from dial-up to broadband, on average across countries, switchers form the dominant category in terms of market penetration (8%), followed by leapfroggers (6%), rather than dual users. In terms of validity, these results make sense because most adopters are unlikely to hold both dial-up and broadband. In contrast, for landline telephones–mobile phone, dual users (24%) dominate on average across countries; in other words, most adopters were keen on holding both technologies ten years from the commercialization of the new technology.Graph: Figure 4a. Decomposition by adopter segments across technology pairs.Furthermore, on average, growth of Technology 2 derived from cannibalization of Technology 1 due to switchers and opportunists is greater than from market growth due to leapfroggers and dual users for the Blu-ray and broadband markets. In contrast, market growth is greater than cannibalization for the other technology pairs. Overall, the results indicate the size of adopter segments and the effects of leapfrogging and cannibalization vary across categories. Are Adopter Segments Similar Across Countries?Following marketing research discussing cross-country effects with multiple data sets (e.g., [19]; [29]), we examine if adopter segments vary across countries. We classify countries in our data set into developing and developed countries. Specifically, we use the analytical classification provided by the World Bank and gathered from various historical reports, as income classifications are rigorous and contemporaneous.[ 8] We term low and low-middle income countries as developing and middle and high-income countries as developed. We present the following results using data from 323 technology pair–country combinations in which we were able to identify the country income classification as of Year 10 from new technology commercialization. We identify 131 cases of high-income countries, 88 of upper-middle income, and 104 of low-income (includes low and low-middle income) countries.The mean estimated penetration of Technology 2 ten years after the new successive technology commercialization is 18% for low-income countries and 23% for high-income countries. The mean estimated penetration of Technology 1 ten years after new technology commercialization is 24% for low-income countries and 49% for high-income countries. These estimates were very close to the actual penetration data for that year.Overall, the mean for leapfroggers is significantly higher for low-income countries compared to both high-income countries (MeanLlowinc = 7.04, MeanLhighinc = 2.61, t = 4.10, p =.0001, using a two-sample T-test with unequal variances) and upper-middle-income countries (MeanLuminc = 4.21, t = 2.09, p =.038). The mean for dual users is significantly higher for high-income compared with low-income countries (MeanDUhighinc = 16.29, MeanDUlowinc = 6.23, t = 4.97, p <.0001) and upper-middle-income countries (MeanDUuminc = 9.10, t = 3.12, p =.002).Thus, a key empirical generalization from our analysis is that developing countries exhibit a higher level of leapfrogging adoption than developed countries in the early life cycle of the successive technology, whereas developed countries exhibit a higher level of adoption by dual users than developing countries in the early life cycle of the successive technology (Figure 4b).Graph: Figure 4b. Decomposition of adopter segments across income classifications of countries.Overall, we find that adopter segments of successive technologies have some context-dependent variations, validating the need for a generalizable model that managers can use to understand the extent of cannibalization and/or market growth. Analysis of Data on Cross-Country Sales of Technology PairsNext, we examine whether the model fits aggregate sales data. We use historical sales data (units in thousands) on three contemporary technology pairs (laptops–tablets, DVD players–Blu-ray players, and digital cameras–smartphones) from 40 countries, with 92 product–country combinations in total for the years 1990–2017 from the Euromonitor Passport database[ 9]. Fit statisticsTable 4 shows the fit statistics. Results indicate that our model with a separately estimated disengagement also fits sales data well. The mean parameter estimates across the 92 product–country combinations are p1 =.02 (SD =.09), q1 =.54 (SD =.34), p2 =.02 (SD =.03), q2 =.29 (SD =.32), p12 =.09 (SD =.12) and q12 =.34 (SD =.33).GraphTable 4. Comparison of Fit Statistics for Sales Data of Technology Pairs. 1 Notes: This table represents the in-sample (training) and out-of-sample (test) error rates for sales data. The explanations are similar to those provided for Table 2. All the raw numbers for this analysis were standardized by the largest observed sales level by each country to provide for a valid comparison by countries. The median error rate refers to the in-sample and out-of-sample error rate across the different countries—using the median instead of the mean—to account for the fact that some countries may greatly influence the averages. Case Analyses of Successive Technology Competitions in the United StatesWe next apply our model to the competition within contemporary, emerging technology pairs in the United States. The application leads to some preliminary generalizations: First, an increase in switchers over time is associated with the cannibalization of sales of Technology 1. Especially when switchers dominate dual users, this increase in switchers is associated with a sustained decline of sales of Technology 1, disrupting incumbents (Cases 1, 4, Web Appendix W4 Case WA1 on digital camera–smartphones). Second, an increase in dual users over time compared with switchers buys time for older technologies and enables them to grow despite the growth of new technologies (Case 2, Web Appendix W4 Case WA2 on VCRs–DVD players). Third, an increase in and dominance of leapfroggers over time is associated with the growth of Technology 2 (Cases 2, 3, Web Appendix W4 Case WA1). Incumbents underestimate or ignore these entirely new consumer segments. Christensen mentioned this, but we show how to estimate its size and evolution. Case 1: Music CDs versus digital downloadsCDs were the dominant music format in 2004, and Apple iTunes' music store had been offering legal digital music downloads since 2003. Although most music executives then believed that people would pay for legal online music, big record labels were slow in adopting digital downloads. Some industry analysts predicted that digital music would not replace CDs because either potential buyers would use it only to sample music before buying CDs or it would only be the terrain of teenagers using iPods ([ 9]). According to analyst expectations, digital downloads and CDs could be expected to grow in tandem. A pertinent question in 2004 was whether digital downloads would eventually cannibalize and disrupt music CDs or if both would in fact grow in tandem.We analyzed data on sales (in millions of units) of music CDs (CDs and CD singles from 1983 to 2018) and digital downloads (including singles, music albums, and music videos from 2004 to 2018) from the Recording Industry Association of America. The analysis from our model (Figure 5a) suggests that switchers (red line) dominated other segments right from the beginning, and this segment grew over the years. Both dual users (orange line) and leapfroggers (green line) tapered off by Year 5. Thus, contrary to the analysts' early expectations, our model indicates that the technologies did not coexist. The immediate high cannibalization by switchers was associated with and probably responsible for the relatively rapid decline of music CDs.Graph: Figure 5a. Decomposition of music CDs and digital downloads in the United States.The decline of music CDs from 2005 caused both record labels and music retailers to suffer. About 800 music stores closed in 2006 alone ([33]). Case 2: Tablets versus laptopsWhile PCs and laptops were the dominant older technologies, the tablet, which was in the works for many years, took off with the introduction of the Apple iPad. At the D8 conference in 2010, when Walt Mossberg asked Steve Jobs whether he thought the tablet will replace the laptop, Jobs replied ""PCs are going to be like trucks. They are still going to be around, they are still going to have a lot of value, but they are going to be used by one out of X people...Is the next step the iPad? Who knows? Will it happen next year or five years from now or seven years from now? Who knows? But I think we're headed in that direction"" ([27]). HP dominated the market for the older technologies, but in 2011, CEO Leo Apotheker wanted to get HP out of the PC business ([12]). ""The tablet effect is real,"" Apotheker is reported to have said on the call with analysts, ""consumers are changing how they use PCs."" Apotheker was soon ousted, and the decision was reversed. A pertinent question at this time was whether tablets would eventually cannibalize and disrupt sales of laptops (and PCs).We analyzed U.S. sales data of laptops and tablets from Passport Euromonitor. Figure 5b shows that while leapfroggers (green line) were the dominant segment, switchers (red line) dominated dual users (orange line) in the first ten years, vindicating HP's initial bleak assessment. However, soon after, dual users (using both technologies) dominated switchers. Our analysis indicates why tablets would not immediately disrupt the market for laptops. Apple gained by attracting dual users while also capturing an entirely new adopter segment base: leapfroggers.Graph: Figure 5b. Decomposition of laptop and tablet sales in the United States. Case 3: Hybrids versus all-electric carsNext, we examine the case of hybrid cars versus all-electric cars.[10] When Tesla first commercialized the electric vehicle, senior managers and analysts scoffed at the idea for three reasons: ( 1) no domestic firm had successfully introduced a new automobile for a hundred years; ( 2) automobile manufacturing is asset-intensive, making the break-even point unacceptably high; and ( 3) California was a state with very high labor costs, especially in comparison to Japan, Korea, and China. To resolve these issues, the critical question for the entrant and the incumbent was whether to invest in hybrid cars, all-electric cars, or both.To answer this question, we use our model to decompose U.S. retail car sales (in thousands of units) of hybrids (including plug-in hybrids) and all-electric cars, obtained from the Transportation Energy Data Book in the time interval 2000–2018. Results in Figure 5c indicate that the growth of all-electric car sales is driven by a predominance of leapfroggers (green line), while switchers (red line) also grow, albeit slowly. Because all-electric cars represent an emergent technology, we have only eight years of new technology data up to 2018. We use data until 2018 and predict two years ahead. Our model predicts that sales of electric cars would cross sales of hybrids in 2020 (two years ahead), driven predominantly by leapfroggers.Graph: Figure 5c. Prediction in the hybrid and electric car market in the United States.Investors may be anticipating Tesla to dominate this race. Before the COVID-19 crisis overtook global markets, Tesla reached a market valuation of $102 billion in January 2020, trailing only Toyota ([31]). In July 2020, Tesla was worth more than Toyota ([30]). Investors are putting pressure on leading incumbents in gasoline and hybrids to invest in all-electric ([10]). Case 4: Taxis versus ride-sharing services in New York CityWe next examine the emergent technology of ride-sharing services such as Uber and Lyft. Because the data for this case were available only for New York City, we limit our analysis to only this city. In many American cities, including New York, drivers need a medallion to operate a taxi, and the city issues a fixed number of them. The ride-sharing service Uber arrived in New York in 2011. Ride-sharing services match passengers with drivers typically through smartphone apps and provide estimated time of arrival, driver tracking, prepayment, and driver and passenger rating. Under pressure from taxi service providers, regulators and politicians sought to regulate or limit Uber's service. The question of relevance in 2012 was whether ride sharing would disrupt taxi services or if they would coexist.We analyze data on trips (in thousands) per day from 2010 on yellow taxis and from 2015 on ride-sharing apps.[11] Our analysis (Figure 5d) reveals an increase in cannibalization over time on the rides for yellow taxis due to switchers to ride-sharing services (red line). However, leapfroggers (green line) and dual users (orange line) also contributed to the rise of ride sharing. Thus, ride-sharing services grew by also attracting a whole new segment of consumers. Anecdotally, it seems ride-sharing services have responded to the needs of customers that previously had difficulty availing themselves of taxi services, including low-income consumers and those in remote locations, as well as individuals who are comfortable with app-based technologies. Over time, switchers ended up dominating the other two segments for ride-sharing apps, contributing to the decline of yellow cabs.Graph: Figure 5d. Decomposition of trips by yellow taxis and ride-sharing services.The cannibalization of taxicabs by Uber, Lyft, and other such ride-sharing services led to a crisis for taxi services. Medallion prices plunged, and the stock of Medallion Financial (a publicly traded company that manages loans used to purchase taxi medallions in several large U.S. urban markets, including New York) had gone down nearly 49% since Uber raised its Series C funding, according to an analysis done by [ 3]. Discussion Summary of FindingsFirst, technological disruption is frequent, with dominant incumbents failing in the face of takeoff and growth of a new technologies. However, disruption is neither always quick nor universal because new technologies sometimes coexist as partial substitutes of the old technology. Our generalized model of diffusion of successive technologies can help marketers capture disruption or coexistence due to the presence of a rate of disengagement from the old technology (0–1), which can vary from the rate of adoption of the new technology (F12 ≠ F2).Second, the model enables a superior fit to aggregate penetration and sales data over prior multigenerational models that do not include such flexibility (i.e., they force F12 to equal F2). Furthermore, an added benefit of the generalized model is that when the rate of disengagement from the old technology equals the rate of adoption of the new, it reduces to a model of multigenerational diffusion.Third, we identify four adopter segments that account for competition between successive technologies from aggregate data: ""leapfroggers"" correlate with the growth of the new technology, ""switchers"" and ""opportunists"" account for the cannibalization of the old technology, and ""dual users"" account for the coexistence of both technologies.Fourth, the generalized model can capture variations in segment sizes across technologies and markets. Leapfroggers form a dominant component of adopters in the early life cycle of a new technology in developing markets compared with other segments. Dual users form a dominant component of adopters in the early life cycle of a new technology in developed markets compared with other segments. Strategic ImplicationsThe major strategic implications of our findings are as follows: First, many established incumbents stumble or fail due to a takeoff of a new technology. Our model can provide important signals about disruption and survival by estimating cannibalization versus coexistence and forecasting the evolution of four critical consumer segments from aggregate data. Incumbents often wait until the market for the new technology is large enough to be profitable ([ 6]) before committing resources to its development. Our analysis suggests that senior managers of strategy and managers of new products should be careful not to underestimate cannibalization by switchers, especially when they dominate dual users, or growth of new technologies due to leapfroggers (especially in developing countries).Second, despite its frequent occurrence, disruption is not a given when a new successive technology enters the market. Thus, managers do not have to make a stark choice between the two technologies. Disruption may be averted by effectively targeting dual users and by carefully examining factors driving the prolonged (co)existence of the old technology.Third, the profit implications of leapfrogging and cannibalization vary depending on which firms market which technology. All segments represent a real gain for entrants, as the takeoff of the new technology is always a win. For the incumbent not introducing the successive technology (e.g., HP), the takeoff of that technology is always a loss. Particularly, if the incumbent firm markets the old technology and a new entrant markets the successive technology, then leapfrogging and switching represent a net loss to the incumbent and a net gain to the entrant. For the incumbent introducing the successive technology (e.g., Sony in DVD players), the takeoff of the successive technology is a win if competitors would have introduced it or if the successive technology has a higher margin than the old technology. Leapfroggers are an opportunity loss for incumbents, but switchers are a real loss to incumbents. If the incumbent firm markets both technologies and if the margin on the new exceeds the margin on the old, then switching and leapfrogging represent a net gain to the incumbent. However, if multiple firms market each technology or if margins vary, then the rate of leapfrogging and cannibalization becomes critical to ascertain profitability given the costs.Fourth, marketers may be able to develop forecasts on the basis of early sales or penetration data of the successive technologies, or from similar contexts, to understand how these various segments may grow (or shrink) over time. Such an understanding can help guide a firm's managerial and economic resource allocation strategies across both technologies over time.Table 5 summarizes the major strategic implications of this research.GraphTable 5. Adopter Segments, Firm Type, and Market Outcomes in the Presence of Multiple Technologies. 2 Notes:3 a Assumes that the incumbent (or incumbents) dominated the market for the old technology and entrants pioneered the new technology.4 b Assumes that the incumbent chooses to enter the new technology market rather than wait on the sidelines.5 c Neutral for adoption/lose if sales is considered. Limitations and Future DirectionsThis study suffers from several limitations. First, we used aggregate data to test the model because they were abundantly available. As managers and researchers get access to richer, individual customer-level data, they may be able to provide better support to our modeling insights. Moreover, disaggregate choice models can be utilized to address issues such as cannibalization. However, macro diffusion models still have the ability to produce useful macro-level conclusions in ways that micro approaches sometimes cannot. Second, we consider a demand-based view of disruption in proposing the typology of adopter segments. Future research could complement these typologies and data sets with surveys to determine the characteristics of adopters of the new technology versus those who stay with the old technology, as well as what factors influence the size of adopter types. Third, an incumbent may respond to the new technology by making changes in variables such as price, and the omission of such control variables may violate some of the assumptions of the model. All these remain fruitful areas for future research. " 37,Leveraging Cofollowership Patterns on Social Media to Identify Brand Alliance Opportunities," The use of cobranding and brand extension strategies to access new markets has been a topic of significant interest. However, surprisingly few studies have examined cross-category connections of brands using publicly available digital footprints. In this study, the authors introduce a new, scalable automated approach for identifying potential cobranding and brand extension opportunities using brand networks derived from publicly available Twitter followership data. The digital user–brand relationship, established through followership activity, is regarded as an expression of interest toward the brand. Common followership patterns between brands are then extracted to capture cointerest between those brands' audience. By utilizing the cointerest patterns, the approach aims to derive cross-category brand–brand and brand–category connections, which can serve as important measures for assessing cobranding and extensions opportunities. This article introduces a new construct, transcendence, which measures the extent to which a brand's followers overlap with those of other brands in a new category. The analysis is conducted at different points in time to help managers track shifts in brand transcendence.","Cobranding is a brand alliance strategy to bolster reach, awareness, and sales potential by tapping the prospective customers of partnering brands. Many types of cobranding schemes exist in the marketplace, including joint advertising campaigns (e.g., ads depicting the joint consumption of Coca-Cola and McDonald's), cause–brand alliances (e.g., UNICEF and Target), bundling (e.g., streaming deals that include joint Hulu and Spotify subscriptions), and cobranded products (e.g., Louis Vuitton launching an exclusive luggage line for BMW). Cobranding strategies enable brand extensions, with managers leveraging the existing brand names of their partners to enter new markets and categories ([14]). Cobranding is increasingly viewed as a valuable marketing strategy and has been shown to increase awareness, quality, market value, and brand equity ([ 8]; [46]). Although marketers have been leveraging the synergistic benefits of cobranding for decades, surprisingly little empirical research has tried to identify potential cobranding alliances using modern digital approaches. Most of the existing empirical research either uses observations from fast-moving consumer goods categories ([ 8]; [14]; [19]) or conducts analyses within a single category, such as camcorders in [24], car brands in [34], and LED TVs in [42]. Similarly, [36] and [35] use recommendation hyperlinks between Amazon web pages to create a large-scale network of books and demonstrate the value of shared purchasing patterns.Obtaining broader insights into the identification of cobranding opportunities across diverse categories would generate relevant and meaningful information for brand owners. As [43] notes, ""By mashing up two bona fide brands, especially in diverse industries, the impact can be exponential."" For instance, a well-known cobranding deal between Starbucks and Spotify—two seemingly unrelated brands—enabled both brands to cross-promote their products and grow their customer base. By providing premium coffee-shop music, Starbucks incentivized Spotify users to join its loyalty program. In return, Spotify grew its user base through Starbucks' offer of a free coffee upon joining. Having knowledge about relevant cross-category brand connections is crucial to brand owners ([12]); however, there is little or no research on identifying these broader cross-category effects using current digital approaches.This article introduces a new, scalable approach for generating cross-category branding insights using implicit brand networks on social media. The cross-category branding insights are revealed in the form of brand–brand and brand–category connections, which can serve as important measures for assessing cobranding and extensions opportunities. Unlike traditional social networks, which involve explicit interaction between the participating entities,[ 5] edges within a brand network are implicit (or tacit) and arise due to common followership between brands. [45] note the relevance of these tacit connections to decision making. This idea has been studied previously within the domain of collaborative filtering ([45]). Implicit networks, which condense the vast digital interest space of millions of users into a parsimonious form, provide direct insight into the digital ecosystem and are the subject of increasing research attention across domains ([45]). In this study, the cross-category connections of a focal brand in the implicit network are leveraged to help brand managers identify cobranding and extension opportunities.The article introduces a new construct, called ""brand transcendence,"" which is defined in the context of a large ecosystem of brands belonging to different categories. The transcendence of a brand into a new category is the extent to which its followers overlap with those of other brands in the new category. From a managerial perspective, this study provides an automated approach for identifying cross-category cobranding opportunities based on user cointerest, which is measured through overlap of brands' followers on Twitter. Importantly, the cointerest patterns captured through common followership do not necessarily reflect overlapping brand associations or guarantee brand fit, which is traditionally measured using the similarity of brand personality dimensions ([48]). However, such patterns are indicative of common tastes or interests among social media users. Following [39], we consider that the composition of a brand's follower base represents the tastes (and likes) of its audience. Thus, the greater the network overlap between two brands, the greater the similarity in tastes and interests between those brands' audiences. Taking these principles together, we study the transcendence of a brand into a new category based on the extent to which its followers overlap with those of other brands in the new category. Our approach also identifies central brands that have strong and consistent connections within their own category ([ 9]), with ""centrality"" being defined as the extent to which a brand's followers overlap with other brands in its own category.By incorporating directionality into the network edges, we also capture the asymmetric relationships between brand pairs, which help identify brands that may potentially benefit more from a cobranding alliance. We outline how cross-category connections can provide both brand–category and brand–brand insights, depending on a brand's marketing goals (i.e., extension vs. cobranding). For instance, brand–category connections capture the transcendence of brands into new categories and show that certain categories are more viable for extensions than others. Brand–brand connections, in contrast, provide a more granular view of transcendence by revealing the individual brands that are suitable for cobranding. As user–brand relationships on social media may change over time, this article analyzes the brand network in both 2017 and 2020. This helps visualize the fluctuations of brand connections over time and investigate the impact of such fluctuations on cobranding alliances. Understanding whether critical connections with certain brands or prospective categories have waned helps managers promptly identify the problem and take appropriate action. Similarly, identifying new connections that have formed over time illustrates how past marketing actions can impact a brand's transcendence in users' minds.Cross-category connections revealed through the network can be used to both assess the effectiveness of previous marketing campaigns and discover new alliance opportunities. For example, Bud Light's connection to Pepsi reflects the cointerest patterns between the two brands and, thus, affirms the effectiveness of joint marketing campaign led by the two brands previously. Similarly, Sierra Nevada's strong connections with travel and technology brands (e.g., Southwest Airlines, Discovery, SpaceX, Microsoft) highlight strong cointerest with these brands and present new cobranding opportunities that may not yet be known to its owners. We provide examples of both scenarios using information from external industry sources. Another practical application of our method is competitor analysis, which can help managers identify the differentiating connections of brands with respect to their competitors and gauge the type of users their competitors attract.Finally, we validate the findings of our model against external survey ratings and conduct extensive robustness checks, including network simulations, to ensure that our final network estimates are not biased by fake users or bots. Consistently high correlation between our automated approach and external survey ratings affirms the validity of our methodology for identifying cross-category brand–brand and brand–category connections. Overall, the core contribution of this study is a new digital approach to analyzing audiences' interests across a broad brand ecosystem. The cross-category insights generated by this approach can help researchers and practitioners identify nontraditional branding opportunities that are difficult to infer from traditional survey-based approaches. From a managerial perspective, our brand network can efficiently and cost-effectively generate cross-category insights, given that most of the data collection and network analyses are automated. Furthermore, as our approach uses information that is publicly available on social media, it is easily scalable to a large number of brands, with the resulting network structures reflecting the preferences of a diverse set of users. In the next section, we discuss relevant studies in the marketing literature and describe how our work contributes to the field. Conceptual Motivations Social Networks for Cobranding and ExtensionsResearchers regard cobranding as a source of competitive advantage that helps brands differentiate themselves, gain consumer trust, acquire new channels of distribution, and enter new markets ([47]). Brand extensions (i.e., leveraging the existing brand's name to enter into new categories) are another widely adopted strategy for firms entering new markets ([ 1]). Brand extensions provide greater quality assurance to customers who are familiar with the original brand, reduce the costs of distribution, and increase the efficiency of promotional expenditure ([ 1]). Both brand extensions and cobranding strengthen the focal brand and reinforce customers' value perceptions of the new product ([20]).Naturally, identifying underlying brand-to-brand connections on the basis of users' cointerests may be a key that enables brand managers to discover potential cobranding and extension opportunities. Most studies in this domain have used surveys ([ 5]). Although collecting input from prescreened participants is desirable, recruiting and maintaining a pool of such participants may be unfeasible due to cost or other constraints ([11]). Recent advances in social network analysis have enabled a wide range of scalable solutions that go beyond conventional market research methods. Although previous studies have considered the identification of brand-to-brand connections based on digital user traces, such research has been restricted to brand (or product) relationships within a single category. For example, [34] focus mainly on intracategory connections to create competitive market structures for car brands. Using survey approaches, [13] obtain intracategory maps for centrality and distinctiveness. Finally, [42] develop mapping methods to visualize large market structures within a single category (i.e., LED TVs).The marketing literature acknowledges the importance of cross-category brand connections for generating extensions, licensing, and cobranding deals ([22]; [41]). However, only limited empirical work has been done in this area. This article introduces an automated, scalable approach for identifying cross-category brand cointerest patterns by leveraging the cofollowership data on Twitter. The use of common followership data on Twitter in our analysis follows the recent work of Culotta and Cutler (2016). However, while [11] aimed to derive perceptual attribute ratings from Twitter followership data, the goal of this work is to investigate asymmetric cross-category brand transcendence over time. Further, unlike Culotta and Cutler, our large-scale network approach does not require any supervised knowledge on exemplars and uses categorical affiliations of brands to infer brand perceptions on transcendence and centrality. Specifically, the inclusion of network-derived measures enables us to study both within-category competition and across-category complementarity between brands. Table 1 presents the unique contribution of this research compared with previous network studies.GraphTable 1. Comparison of This Study with Previous Network Studies in Marketing. Cofollowership Patterns for Identifying Cobranding and Brand ExtensionsOur approach to identifying cobranding and brand extension opportunities harnesses the digital cofollowership patterns between brands. Survey research has shown that users follow brands on social media with the intention of purchasing a product or learning more about their favorite brands ([31]; [40]). Aspirations can also motivate consumers to follow brands ([ 3]). This digital user–brand relationship, which is established through followership activity, can be interpreted as an expression of affinity for the brand ([27]; [33]). Alternatively, this relationship can be viewed through the lens of homophily, meaning that people tend to associate with those who are similar to them in socially significant ways ([32]). This is further supported by consumer research studies ([ 6]; [10]), which show a strong relationship between a brand's image and characteristics and the identities of its followers.Similarly, [39] find that the composition of one's follower base represents the tastes (and likes) of their audience. Thus, the more network overlap (i.e., common followers) between two entities, the greater the similarity of tastes and interests among those entities' audiences ([39]). [ 2] find that common friends (i.e., common mutual followers) have a positive effect on the adoption of an application on Facebook. Similarly, we expect that brands that share a high number of followers on Twitter have a user composition that represents similar tastes or interests (e.g., the partnership between GoPro and Red Bull, which leveraged the shared affinities of their common followers: action, adventure, and fearlessness). Given that individuals primarily follow a brand because they like its products and that most followers are customers ([40]), brands having more common followers implies that their customers may have complementary consumption patterns (e.g., the Starbucks–Spotify partnership, which facilitated the complementary consumption of Spotify music and Starbucks coffee). Building on these theoretical principles, we study the cobranding candidates of a focal brand based on the extent to which its followers overlap with the followers of another given brand (and/or category) of interest. Brand Transcendence and CentralitySome of the greatest brands in the world have defied category norms and transcended their initial market boundaries ([23]). For example, in 1995, Amazon positioned itself as ""Earth's biggest bookstore,"" and its success with books enabled it to transcend its origins to become a leader in e-commerce. Although all brands theoretically operate within their categorical boundaries, such boundaries are often considered malleable ([ 7]). Important cobranding and extension opportunities can be missed if managers are not aware of connections that are relevant to brands in other categories ([ 5]). The brand network provides a solution to this problem by relying on a brand's social connections on Twitter to infer category-specific brand connections.At a high level, our proposed algorithm extracts the category-specific connections of a brand by exploiting the overlap in brand followers on Twitter. Whereas some brands may possess strong connections within their own category, others may have diverse connections across new categories. This article introduces a new construct, transcendence, which measures the extent to which a brand's followers overlap with those of other brands in a new category. The transcendence of a nonsports brand along any given category—for example, say sports—is based on the extent to which its followers overlap with those of other brands in the sports category. Further, to measure the connections not shared by the overall brand category, we calculate ""net transcendence"" as the deviation of a brand's own idiosyncratic connections from its category average. Net transcendence is more informative than raw transcendence because it ignores the cross-category connections that are generic to the category and identifies those that are intrinsic to the brand itself.Lastly, in addition to transcendence, there are brands that possess strong connections within their own category. These brands can be viewed as central. The concept of centrality (or typicality) bears direct relation to a brand's probability of recall, consideration, and choice among consumers' minds ([28]). Such central brands are those that come first to consumers' mind and serve as reference points in their categories ([13]). In the next section, we describe how the brand network is generated from followership patterns on Twitter and lay out important network details. The Network Mining MethodologyThe key contribution of this article is the introduction of an automated framework for inferring cross-category branding insights using implicit brand networks derived from social media. With their ability to provide a direct digital window into the interests of millions of social media users, implicit brand networks can help mangers identify nontraditional branding opportunities that would otherwise be hard to perceive. In this section, we generate implicit brand networks using brand communities on Twitter and outline important network details. DataDrawing from the notion that the social signal of ""who follows a brand"" provides a strong reflection of brand image ([11]), we use a set of 507 brands' Twitter accounts as the basis for our analysis. We select the most active Twitter brand accounts based on followership data from the social media directory FanPageList.com. We use Twitter's public application programming interface to collect the brands' lists of followers for 2017 and 2020. We manually verify that all Twitter handles correspond to the official brand account. Overall, the data set consists of brands from many major categories: airlines, luxury goods, retail, automotive, sports, technology, dining, food and beverages, lodging, media, travel, cruises, and beer. Each brand is assigned to a specific category based on the basic or superordinate category-level analyses ([28]).[ 6] To prevent bots or spam accounts from influencing the network analysis, all Twitter brand accounts included in the analysis are manually audited using the audience intelligence website SparkToro.[ 7] Furthermore, as we discuss in the section on robustness checks, we conduct network simulations to ensure that our final network estimates are not biased by such bots. Network GenerationThe next step is to extract the common followers between all brand pairs. The raw brand network is a weighted edge list, defined as 〈bi, bj, wij〉, where bi and bj are individual brands or nodes and wij is the common followers between those brands. If Fi and Fj represent the list of Twitter accounts following brands bi and bj, then an edge between two nodes is created if Fi ∩ Fj > 0. Alternatively, the weighted edge list can be represented as a weighted adjacency matrix Aij where Aij=wij0}ifbrandiandbrandjareconnectedotherwise. GraphOverall, we extract two brand networks: one for 2017 and one for 2020. The original brand networks are highly dense, with common followers between almost all pairs of brands. The numbers of common followers vary from a few hundred to more than a million users. Although it is possible to work with such dense networks, valuable information may be lost due to the redundancy generated by the large number of connections ([44]). Further, connections based on too few followers may not indicate significant connectivity. Given the wide heterogeneity in raw edge weights (i.e., the numbers of common followers), we next aim to extract the truly relevant brand–brand connections. Network filteringA common way to extract a relevant network structure is by applying a global threshold to remove the edges with weights below a particular cutoff. This, however, can destroy the multiscale properties of the brand network. Instead, we use a disparity filter ([44]), which is a filtering algorithm for multiscale networks, to obtain a reduced but more meaningful representation of the network. This method preserves the important edges present at all scales by locally identifying the statistically relevant weights at the node level. The statistically relevant edges (at a given significance level; e.g., α) represent a significant deviation from a null model of uniform randomness. Thus, smaller brands with fewer common followers are not ignored during the network reduction process. Although the current analysis focuses on Twitter brand accounts with between a few thousand and more than a million followers, this method could also be applied to smaller brands with fewer than 1,000 followers. Following [44], we use the commonly specified significance level α = .05 to extract the important connections in the brand network. The filtered networks for 2017 and 2020 consist of roughly 14,000 edges between brands. Although we use the disparity filter to obtain a filtered representation of the original network, there are alternative information-filtering algorithms available in the network science literature, including the global threshold and global statistical significance filters. In Web Appendix A, we revisit these alternative methods and discuss our rationale for choosing the disparity filter. Asymmetric normalizationBrand community sizes can vary both within and across categories. Brands with large brand communities (e.g., Chanel, Microsoft, Starbucks) tend to have more common followers than those with smaller communities. The normalization of edge weights is required to account for this variance. Thus, we use the conditional probability measure ([42]) to compute new network weights that not only normalize the effects of brand size but also account for asymmetry between brand pairs. Asymmetry between brand pairs may occur when the degree of connection between any two brands is unequal (i.e., the connection from A to B is not equal to the connection from B to A) ([15]). Ignoring the directionality of brand connections can lead to inaccurate estimates of consumer brand knowledge ([16]). We observe many cases of associative asymmetry in our brand network and use conditional probability to account for such scenarios. For instance, Figure 1 shows the cross-category connection between Starbucks and Stella Artois. A large percentage of Stella Artois fans are interested in Starbucks, and the outgoing directional strength is almost.20. However, fewer Starbucks fans are interested in Stella Artois, and the outgoing directional strength is comparatively much lower, at.0009. Incorporating directionality in the network reveals this crucial information, which is not visible in a simple, undirected, weighted network. Mathematically, the conditional probability measure calculates the normalized edges between any two brands A and B as P(A∩B)=|A∩B||A|, Graphwhere the numerator |A∩B| is the number of common followers between brands A and B and the denominator |A| is the number of followers of the focal brand.Graph: Figure 1. Calculating asymmetry between brand pairs.Figure 2 shows the entire brand network structure for 2020 using the dimensional reduction algorithm, t-distributed stochastic neighbor embedding (t-SNE). The t-SNE algorithm yields a two-dimensional undirected representation of the brand network, with the distance between brands in the t-SNE space being proportional to the mean conditional probabilities between brands. The colors of the brands correspond to their category affiliation. Interestingly, while most automotive brands in Figure 2 are well-distanced from other nonautomotive brands, Tesla is positioned in the technology category. A similar pattern is observed for certain retail brands such as Adidas and Reebok, which are closer to the sports group than to other brands in their category. Individual brand constructs on transcendence, as described in the next section, help reveal specific cross-category connections for a given brand.MAP: Figure 2. t-SNE map of the undirected brand network.[ 8] Measures of Brand Transcendence and CentralityThis section outlines the process of identifying centrality and transcendence by exploiting the connections of a brand within the network. For any given brand, the first step is to disentangle its connections across the main categories: airlines, luxury goods, retail, automotive, sports, technology, dining, food and beverages, lodging, media, travel, cruises, and beer. The algorithm then computes the weighted out-degree centrality of a brand across these categories. In weighted networks, out-degree centrality or node strength is commonly calculated as the sum of weights emanating from a focal node to all its connections ([ 4]). However, to account for the strength of edge weights and the number of connections of a focal node, [37] propose a new measure of weighted degree centrality: Ci=di(1−α)×wiα, Graph( 1)where di is the degree of the focal node (i.e., the number of connections), wi is the node strength (i.e., the sum of the weighted connections), and α is the tuning parameter from 0 to 1. For example, following [37], the transcendence of a given nonsports brand into the sports category is calculated as the function of its number and strength of outgoing connections to all other brands in the sports category.More formally, the set of brands in the network can be represented as B, where any individual brand b∈B . Brand categories, G, are subsets of B, such as G ⊆ B.[ 9] The transcendence of focal brand b onto a new category G is evaluated as tbG=∑k∈Gdb,k(1−α)×∑k∈Gwb,kα|G|, Graph( 2)where ∑k∈Gdb,k is the number of outgoing edges from brand b to all k brands in category G, ∑k∈Gwb,k is the sum of weighted edges from brand b to all k brands in category G, and |G| gives the number of brands in category G. Dividing by the total number of brands in a category, |G| , helps ensure that large categories with many brands do not dominate the analysis. Following [37], we set α to.5 to place equal importance on a brand's number of connections and the weight of those connections.Furthermore, considering that brands whose followers tend to follow many other brands may inflate the network constructs, we divide our transcendence construct by the total degree of a brand in the network (i.e., its number of connections to other brands). Intuitively, brands whose followers tend to follow many brands have a higher degree than brands whose followers follow fewer brands. Thus, in the transcendence construct, the sum of the numerator increases with each new connection of a brand in the network. With the objective of normalizing for brands that inherently have higher aggregate transcendence due to higher degree in the network, we divide our final transcendence construct by the total degree of a brand. Thus, the final transcendence construct becomes tbG=∑k∈Gdb,k(1−α)×∑k∈Gwb,kα|G|×db, Graph( 3)where the additional variable in the denominator, db, is the total number of connections of a brand in the network.Furthermore, in our transcendence construct, brand b's connections within its own category Gp relate to its centrality ( tbGp ): the higher the strength of these connections, the more central the brand is in its own category. As noted previously, the notion of centrality is directly related to a brand's probability of being recalled, considered, and chosen by consumers ([28]). We multiply the centrality construct by the size of the focal brand's community (i.e., its number of followers) to account for the brand's popularity among users. Thus, the final centrality construct is tbGp×fbmax(fbi∈Gp), Graph( 4)where tbGp is brand b's connections within its own category Gp and fb is its number of followers. Given that the values of fb vary from a few hundred to more than a million users, we use its scaled value. Given a set of nonoverlapping categories G1, G2, ..., Gp, the transcendence of brand b across p categories is a 1 × p-dimensional vector: tb=[tbG1tbG2tbG3⋯tbGp]. Graph( 5)The transcendence vector of a brand can also be analyzed with respect to its competitors in the category. The 1 × p-dimensional vector tb can be further extended into an n × p matrix, where n rows represent brands (i.e., b1,b2...,bn ) and p columns represent transcendence across the p categories, as shown in Figure 3.Graph: Figure 3. The transcendence matrix, tbG for n brands across p categories.The average category connections (i.e., connections emanating from one category Gi to another category Gp ) are then calculated as follows: tGiGp=∑b∈GitbGp|Gi|, Graph( 6)where i≠p . This formula measures the average transcendence of brands in one category (for example, Gi ) into another category Gp. To separate a brand's unique connections ( tbGp ) from its category average ( tGiGp ), we calculate the net transcendence of brand b into category Gp as follows: t_netbGp=tbGp−tGiGp, Graph( 7)where b∈Gi and i≠p . A positive value for t_netbGp indicates that the brand's transcendence is above the category average, while a negative value indicates that its transcendence is below average. As in the raw transcendence vector tbGp , the net transcendence vector of a brand b across p categories is t_netb=[t_netbG1t_netbG2t_netbG3...t_netbGp]. Graph( 8)The 1 × p-dimensional vector t_netb can be further extended into an n × p matrix, where rows represent n brands in a category and p columns represent the net transcendence of brands across the p categories, as shown in Figure 4.Graph: Figure 4. Net transcendence matrix of n brands across p categories.Figure 4 provides a more comprehensive view of the competitive landscape of a particular brand by highlighting its cross-category connections as well as those of its competitors. In the next section, we discuss the results of these analyses and identify their key managerial implications. Results and Managerial ImplicationsDepending on the business objective, the category-specific connections generated by the brand network can be visualized on two different levels: brand–category and brand–brand. Using examples from the automotive and beer categories, in this section we present our results on these two levels and discuss the managerial implications of our findings. First, we note the brand–category connections of automotive brands and identify the categories suitable for brand extensions. Second, we focus on brand–brand connections and discuss how asymmetry can be leveraged to attain more nuanced insights into the expected benefits of cobranding. Third, we highlight the network's ability to capture changes that occurred between 2017 and 2020, given that individual brand–brand connections may change over time. Finally, we discuss how the brand network described in this article can help managers identify the differentiating category-specific connections of brands with respect to their competitors and gauge the type of users their competitors attract. Brand–Category Connections: Transcendence and CentralityTo identify the brand–category connections of automotive brands in 2020, we study the net transcendence matrix, t_netbGp , as shown in Figure 5. All column values have been scaled, with positive values shown in red coloring and negative values shown in blue on the heatmap. For every brand (n)–category (p) relationship in the heatmap, values closer to dark red indicate a stronger perceived relationship between a brand and category. The stronger the relationship between the brand and category, the greater the user cointerest between that brand and category. As discussed previously, the cointerest patterns captured through the analysis of common followership do not necessarily guarantee brand fit, which is typically based on the similarity of brand personality dimensions ([48]). Instead, values in the transcendence matrix reveal cointerest patterns that enable managers to explore potential extension and cobranding opportunities that are difficult to infer from traditional survey-based approaches. For instance, the audience of the car brand Mercedes has strong cointerest with the luxury, technology, retail, and sports categories, suggesting that extensions may be possible in these categories.Graph: Figure 5. Net transcendence matrix, t_netbG, reflecting brand–category connections of the automotive brands (2020).Similarly, there may be brands that, despite having low net transcendence into different categories, have strong connections with their group, making them central in their own category. For example, Toyota and Dodge are highly central to the automotive category, although their net transcendence across categories is low. Tesla, in contrast, has high net transcendence into the technology category, despite having low centrality in the automotive group. We also observe that some car brands with high net transcendence across multiple categories have moderately low centrality in their group (e.g., Audi, Mercedes, Tesla, and Lamborghini). However, brands such as Chevrolet and Ford share strong cointerest in the automotive category and also have moderate net transcendence into beer, dining, and sports. Thus, centrality and transcendence are not mutually exclusive, and a brand may be perceived as both central and transcendent, depending on its connections in the network. Brand–Brand Connections: Leveraging Asymmetry for Cobranding InsightsThe brand network developed in this study can also help managers obtain a more granular view of transcendence by identifying brand–brand connections across categories. The different levels of analysis (i.e., brand–category and brand–brand) offered by the network can help managers understand why certain cobranding opportunities are more promising than others. Further, the network's analysis of the asymmetry between brand pairs can help identify brands which may potentially benefit more from a cobranding alliance. To identify strong, relevant cobranding candidates for a focal brand, we only consider brand–brand connections in categories where the net transcendence of the brand is positive. This ensures that the identified brand connection is not generic to the category but rather is intrinsic to the brand itself. For instance, the net transcendence of Mercedes into the luxury goods and retail categories is positive, meaning that, on average, its connections with luxury and retail goods are relatively higher than those of other car brands. Thus, for Mercedes, brands in the luxury and retail categories are considered suitable candidates for cobranding.Figure 6 shows the brand–brand connections of Mercedes with brands in the luxury and retail categories. Some of Mercedes' strongest connections are with Louis Vuitton, Nike, Tissot, and Chanel, which highlights these brands' potential for alliances with Mercedes. However, given the asymmetrical nature of these relationships, the benefits gained through such alliances may not always be equal. For instance, the asymmetrical connection between Mercedes and Tissot reflects that a greater proportion of Tissot's audience is interested in Mercedes than vice versa. This indicates that there may be a greater potential benefit for Tissot from such an alliance. These results on asymmetry can provide additional insights to brand managers of both Mercedes and Tissot when evaluating potential cobranding candidates.Graph: Figure 6. Top 20 brand–brand connections of Mercedes with brands in the luxury and retail categories using the Fruchterman–Reingold (1991) layout.Indeed, prior strategic alliance literature suggests that unequal spillover benefits can be expected from asymmetrical brand alliances ([21]). However, the main findings of [21] suggest that even though the magnitude of financial gains in asymmetrical alliances is not equal, it is not a win–lose partnership but rather a win–win or a shareholder value-adding alliance for both the larger and smaller partner firms.[10] Similarly, in the case of Mercedes, even though the expected benefits may not be equal for the asymmetrical relationships (e.g., Mercedes–Chanel, Mercedes–Tissot, Mercedes–Rolex), future deals can be still beneficial to both brands. Although the smaller brand, Tissot, may achieve greater gains from this asymmetrical alliance (e.g., by having a greater proportion of its audience interested in Mercedes), the larger brand, Mercedes, may still gain access to a niche audience that may not be a part of its current demographic. Capturing Shifts in TranscendenceA brand's transcendence in the network, which arises from common consumer interest, may not be static. Brands may, for various reasons, wish to shift their transcendence to new categories in search of new alliances or cobranding opportunities. Our network can track such shifts in transcendence, allowing brand managers to better assess the effectiveness of their marketing actions and identify emerging or waning categories for future brand alliances.Figure 7 shows the change in net transcendence of car brands into the technology category. The dynamic plots for other categories can be analyzed similarly. Interestingly, between 2017 and 2020, the technology connections of many car brands, including Honda, Jeep, Chrysler, Acura, and Chevrolet, decrease. However, some brands, including Tesla, Lamborghini, and Infiniti, show a steep rise in their connections with technology. This may be related to, for example, Infiniti's plans to go all-electric in 2021 and use intelligent technologies to reflect its new ethos ([30]). Thus, our brand network-based methodology can be used to assess the effectiveness of a brand's marketing campaign and showcase how marketing actions can impact the brand's transcendence in users' minds.Graph: Figure 7. Change in net transcendence over time.The network's ability to highlight shifts in brand transcendence over time can be of vital use to managers. The emergence of new connections with specific categories over time (e.g., Infiniti's increasing transcendence into the technology category) provides insight into the effectiveness of brands' marketing campaigns and affirms the possibility of future extensions in those categories. Similarly, the waning of a brand's connections with specific categories (e.g., Jeep's decreasing transcendence into the technology category) allows that company to identify potential problems and take appropriate action to address them. The brand network can also help managers investigate issues in more detail by uncovering the specific cross-category brand–brand connections that have diminished over time. Change in a brand's net transcendence into a category can be caused by several factors, including joint ads, new alliances, embedded promotions, or other external events. Although the method in this study does not examine the causes for such shifts, it provides managers with timely intelligence on the subject. Future marketing studies could build on this work to further investigate the causes for changes in brand transcendence over time. Competitor AnalysisThis subsection discusses how the category-specific brand connections revealed through the transcendence matrix may not only allow managers to understand the position of their brands in consumers' minds but also help distinguish them from their competitors. Figure 8 shows the net transcendence, t_netbGp , of two beer brands, Bud Light and Sierra Nevada, into different categories. Whereas Bud Light has high transcendence into food and dining, Sierra Nevada has high transcendence into travel, airlines, and technology. Regarding centrality, Bud Light outperforms Sierra Nevada, with stronger connections within the beer category. Thus, whereas the former brand is positioned strongly among beer and food enthusiasts, the latter brand resonates more with technology and travel enthusiasts. This type of analysis can help brand managers identify the differentiating connections of their brands with respect to their competitors and also gauge the type of users their competitors attract.Graph: Figure 8. Net transcendence matrix, t_netbG, of Bud Light and Sierra Nevada.Brand managers can obtain richer insights into the cointerest patterns of their competing brands by examining the individual brand–brand connections in the different categories. For example, Bud Light is connected to more food, beverage, and dining brands (e.g., Pepsi, Coca-Cola, McDonald's, Subway, Taco Bell), while Sierra Nevada is mostly connected to airline, travel, and technology brands (e.g., Southwest Airlines, Discovery, SpaceX, Amazon, Netflix). As one might expect, some brand–brand connections can reflect previous marketing activities (e.g., joint advertisements or promotions, collaborations, licensing deals). In such cases, our brand network–based methodology enables managers to measure the effectiveness of a marketing campaign and showcases how marketing actions can impact a brand's transcendence. For example, Bud Light's connection with Pepsi reflects strong cofollowership patterns between the two brands, affirming the effectiveness of their earlier joint marketing campaign.[11] Alternatively, brand–brand connections can highlight potential new cobranding or alliance opportunities that were previously unknown to brand managers. For instance, Bud Light's strong connections with McDonald's and Taco Bell highlight strong cointerest between these brands, suggesting untapped cobranding opportunities. Similarly, Sierra Nevada could leverage the technology and travel interests of its fans, as revealed by the network, to partner with relevant travel brands such as Southwest Airlines, SpaceX, Discovery, Amazon, and Netflix. In the next section, we validate our results against external survey ratings and test the reliability of our findings. Survey ValidationTo validate the effectiveness of our methodology, we compare the network ratings from our automated approach with directly elicited survey ratings. The survey was conducted through Amazon Mechanical Turk, which is a reliable source for conducting social sciences research ([11]). The survey respondents were asked to report their income, age, and gender to account for any demographic influence in the sample. The participants were required to be located in United States and be over 18 years old. To ensure high-quality responses, a prior task approval rate of 95% was required for all survey respondents. The brands were grouped by sector, and four separate surveys, consisting of 250 participants each, were conducted to validate the brand–category and brand–brand connections of beer and automotive brands. Next, we discuss our survey findings along with several robustness checks. Validating Brand–Brand ConnectionsIn this subsection, we examine whether the cobranding candidates identified by the network are also perceived by consumers to be such candidates. The network edge weights between brands are intended to reflect consumers' perceptions of the brands that could be paired for cobranding; thus, such a relationship should be reflected in the survey responses. For this validation, we select the five most-followed brands in the beer and automotive categories. Then, for each brand, we select nine cobranding candidates: ( 1) the top three cross-category cobranding opportunities (i.e., brands), as identified by the network, ( 2) the top three most-followed brands in the sample that are not included in part 1, and ( 3) three randomly drawn brands that are not included in parts 1 and 2.For each focal brand, we ask the respondents to rate its cobranding candidates on a scale of 1 (""less likely to go together"") to 10 (""highly likely to go together"") according to how strongly they can be paired with the focal brand. The survey scores were then correlated with the outgoing edge weights from the focal brands to their cobranding candidates in the brand network. For every survey question, the brand order was randomized, and attention filters were included to identify invalid responses. To identify loyal fans, participants were separately requested to select their favorite auto and beer brands from the list. Details of the survey and the corresponding descriptive statistics are included in Web Appendix B. ResultsTable 2 shows the Pearson correlation coefficients between the survey and network scores. Overall, the survey measures correlate well with the network estimates, with the survey's top-three-box score[12] achieving an average correlation of.67 with the network estimates. The overall correlation between average survey ratings and network constructs is.65.GraphTable 2. Pearson Correlation Coefficients of the Survey Estimates with the Network Constructs. In addition, we compute the correlations between network scores and the survey ratings of users who rate a specific brand as their favorite. When using only data from fans, the overall correlation coefficient increases to.70 for average survey ratings and.71 for the top-three-box survey ratings. Finally, we examine the scatter plots more closely to better understand the circumstances in which the network cobranding candidates align well with the survey responses. For example, Figure 9, Panel A, shows the cobranding candidates for Audi as suggested by the network, together with the corresponding survey ratings. The top three cobranding candidates suggested by the network (i.e., Microsoft, Nike, and Intel) also receive very high ratings from the survey respondents. Brands with low network connectivity with Audi (i.e., Lays, Forever21, and ABC) also receive lower ratings from the survey respondents. These results reaffirm the previous findings that network connectivity patterns between brands are useful marketing metrics for consumers' perceptions of which brands are likely to pair well together.Graph: Figure 9. Network versus survey estimates for brand–brand connections of Audi and Budweiser.Figure 9, Panel B, shows the cobranding candidates for Budweiser, as suggested by the network and survey responses. Some top cobranding candidates suggested by the network, including the NFL and Pepsi, also receive high ratings from the survey respondents. However, despite its strong connection to Budweiser in the network, Starbucks receives low ratings from the survey respondents. Although the strong network connectivity between Starbucks and Budweiser is not directly perceived by survey responders, it may indirectly reflect the complementary taste interests of coffee and beer drinkers, which Starbucks previously leveraged to launch a line of beer-like coffee drinks ([26]; [38]). Similarly, Budweiser has a lower network connectivity score with the soccer brand FIFA than the corresponding survey rating. On further investigation, whereas Budweiser's U.S. Twitter account, which was used in our survey validation, has low network connectivity with FIFA, the brand's global Twitter account is very strongly connected to FIFA. This suggests that brand managers should apply domain knowledge and managerial judgment to explore alternative cobranding candidates based on their market of interest. Using domain customization, brand managers can query the brand network to include brand accounts that best suit their target market and conduct a more tailored network analysis to identify where the proposed cobranding opportunity may work well.The preceding analysis also highlights some of the limitations of survey-based validation. First, consumer responses to direct questions on brand perceptions are based on the respondents' existing notions of brand extendibility and may be confounded by prior user experiences ([25]). Second, asking consumers about their overall perceptions using direct rating scales may not reveal novel or unique brand extensions or cobranding ideas; rather, it may simply facilitate the testing of known concepts ([ 5]). The brand network, in contrast, leverages the cofollowership patterns of millions of Twitter users across a broad brand ecosystem to reveal cross-category cobranding ideas that may not be intuitive to consumers but, in hindsight, are effective. Overall, with an average correlation of.71 between network scores and fans' survey ratings, the validation results suggest that the automated network-based approach enables managers to quickly and inexpensively identify cobranding and brand extension ideas that would otherwise be difficult to anticipate. Validating Brand–Category TranscendenceNext, we validate whether the transcendence measures derived from the brand network align with consumers' perceptions of the brands. For both beer and automotive brands, consumer ratings along the luxury and technology categories were elicited. On a scale of 1 (""least likely"") to 5 (""most likely""), participants were asked to rate the focal brands (e.g., Heineken) according to how strongly they associated them with a new category (i.e., luxury goods and technology). Further, to identify brands with strong centrality within their own group, participants were asked to rate the focal brands, on a scale of 1 (""least likely"") to 5 (""most likely""), according to how strongly they believed them to be central in their own category. Finally, the average survey ratings for each brand across the luxury and technology categories are compared with the brand transcendence constructs obtained using the network measures. Similarly, the average survey rating for centrality is compared with the centrality construct obtained using the network measures. We also calculate the top-two-box score for each brand to assess the proportion of people who rate a brand very highly (i.e., a score of 4 or 5). Details of the survey and the corresponding descriptive statistics are included in Web Appendix B. ResultsThe Pearson correlation coefficients between the survey results and network constructs are listed in Table 3. Overall, the survey measures correlate well with the network estimates, with the top-two-box survey scores and mean survey scores achieving an average correlation of.63 with the network estimates.GraphTable 3. Pearson Correlation Coefficients of the Survey Estimates with the Network Constructs. Figure 10 includes the scatter plots for survey versus network measures. Given that the network and survey estimates are measured in different units, the plots have been scaled to 0–1 for easier interpretation. Overall, the scatter points are well distributed along the best fit line, with few outliers. As we discuss next, we then conduct a series of tests to ensure that the network accurately captures the shifts in connections over time. Overall, our general findings pass these tests, supporting the future use of implicit brand networks in marketing research.Graph: Figure 10. Scatter plots of network versus survey estimates for automotive and beer brands. Shift in connections from 2017 to 2020The ""Results"" subsection discusses the brand network's ability to capture shifts in brand transcendence over time. We now test whether the waning of certain connections between 2017 and 2020 in the network is supported by the survey responses. To do this, we first identify the connections between brands and categories that exist in the 2017 network but decline in the 2020 network. Figure 11 illustrates the filtered cases for automotive brands and the corresponding survey results. Panel A shows that brands such as Mazda, Mini, Buick, Chrysler, and GMC all have connections with the luxury category in 2017. According to [17] in Forbes, at the time, the Mazda 2017 CX-9 Signature model was considered the most luxurious vehicle produced by Mazda to date. The author mentions that ""Mazda has never been considered a luxury brand, but maybe it's time to reconsider that classification"" ([17]). However, the results from the brand network in 2020 show that Mazda does not retain its connection with luxury. This is further validated by the survey participants, who also rate Mazda as very weakly associated with luxury. There is a similar pattern in Panel B, in which brands such as Dodge, Chevrolet, Jeep, Honda, and Chrysler show a significant drop in their connections with technology category between 2017 and 2020. This change is also reflected in the survey responses.Graph: Figure 11. Shift in transcendence of car brands from 2017 to 2020. Random connections rejected by survey participantsNext, we test whether survey respondents also reject random connections that do not exist in either 2017 or 2020. To do so, we filter the cases where connections between brands and categories are absent in both the 2017 and 2020 networks and compare them with the survey responses.[13] We find that the average survey ratings are below 2 (out of 5) for most brand–category connections not existing in either network (i.e., 2017 and 2020). Addressing demographic bias on TwitterStudies that mine brand perceptions from social media sources must consider the extent to which brand followers on social media represent the general population. It is also important to consider whether certain Twitter brands accounts are more appealing to a specific audience (e.g., young people, men). Recent studies have reported that Twitter followership data successfully captures attribute-specific consumer perceptions beyond demographic similarities ([11]). We investigate this issue further by comparing the survey ratings, which were provided by users of different demographics, with the transcendence values obtained from the brand network. Table 4 lists the correlation values between the network estimates and income-specific survey ratings for the transcendence of automotive brands into the technology category. For most income groups in the survey, there are adequately high correlations with the network transcendence constructs. Results for all the remaining survey demographic groups (i.e., age and gender) are included in Web Appendix D. We observe adequately high correlations between the demographic-specific survey ratings and the brand network constructs. This affirms that the overall brand network estimates are not heavily influenced by the demographics of Twitter users.GraphTable 4. Income-Specific Survey Correlations with the Network Constructs. Sensitivity of transcendence constructs to network rewiringThe presence of Twitter bots may inflate the number of common followers between brands, which can, in turn, lead to inaccurate network estimates of transcendence and centrality. In this section, we conduct multiple network simulations by repeatedly rewiring the edges to test whether the original brand network structure remains reasonably stable. We incrementally rewire the cofollowers from any random pair of edges and reperform the entire analysis. As Figure 12 shows, the rewiring stage involves the addition and removal of 5% of the cofollowers of any random pair of edges in the network, continuing until 50% of the cofollowership patterns have been altered. In each iteration, once the network rewiring is complete, the algorithm reruns the entire analysis (i.e., it applies the disparity filter to identify statistically significant edges, normalizes the edge weights, and calculates the transcendence across categories).Graph: Figure 12. Process flow for each iteration.Figure 13 illustrates the results of the simulations for the automotive brands and compares the transcendence values obtained after rewiring the network with the original values. The purpose of the test is to ensure that the original network estimates hold for small rewiring changes (i.e., that significant rewiring is needed to yield completely different network estimates). For all plots, the rewired network estimates correlate highly with the original estimates until a large percentage of the network (>30%) is rewired. This demonstrates that the brand network structure is not sensitive to small underlying changes that may occur due to bots.Graph: Figure 13. Correlation of post rewiring transcendence values with original transcendence values for automotive brands. ConclusionDespite its relevance to various marketing decisions (such as cobranding and brand extensions), the identification of cross-category insights across a broad brand ecosystem is currently understudied in the marketing literature. This article uses implicit brand networks to identify the category-specific connections of brands and their competitors by exploiting the overlap in brand followers on Twitter. We introduce a new construct, transcendence, that measures the extent to which a brand shares cointerest with other brands in different categories. Depending on a firm's marketing objectives (i.e., their focus on extensions vs. cobranding), the transcendence of a brand can be studied at different levels: brand–category or brand–brand. These different levels of analysis can help managers identify viable cobranding opportunities.Furthermore, we leverage the concept of asymmetry between brand pairs to provide more nuanced insights into possible cobranding opportunities and determine which brand can potentially benefit more from a cobranding alliance. We conducted the analysis over time to track shifts in brand transcendence, allowing brand managers to both assess the effectiveness of existing marketing strategies and identify new alliance opportunities. To ensure the reliability of our proposed methodology, we validate our findings against external survey ratings and conduct extensive robustness checks, including network simulations, to ensure that our final network estimates are not biased by Twitter bots.From a methodological standpoint, the implicit brand networks utilized in this article condense the high-dimensional interest space of millions of brand followers into a parsimonious form that is more amenable to research and business applications. The readily accessible artifact, which is obtained with little human intervention in the processing of the underlying data, allows managers to efficiently infer cross-category branding insights in a scalable way. Compared with extant digital approaches that rely on extensive preprocessing, this straightforward automated approach enables practitioners to readily obtain the cointerest patterns of brands with respect to their competitors and gauge the types of users that their competitors attract. More specifically, given its automated data collection and network analyses, the brand network can act as an effective business intelligence tool for the identification of cobranding and extension opportunities across a broad ecosystem of brands.Overall, our approach offers several benefits to marketers. It also highlights avenues for future research. First, although our analyses use Twitter brand communities, it would be interesting to compare similar communities on Facebook and Instagram. Brand networks on different social media platforms may vary based on factors such as user demographics, category, platform characteristics, or a brand's marketing strategy. Although consistent brand connections across different platforms can provide additional validity to findings of this study, meaningful insights may also be gleaned if substantial differences are observed. Such differences may, for example, stem from a brand's tailored marketing efforts on a specific platform. Using brand networks to track the effectiveness of such efforts can be beneficial to brand owners. Differing user demographics across platforms may also have an impact on brand network structures. Though this study did not identify substantial differences between the demographic-specific survey ratings and the transcendence values obtained from the brand network, future research could examine platform-specific brand networks to obtain richer insights. Second, future research could consider how to distinguish the content on brand pages that may affect consumers' decisions to follow brands, including promoted content on a brand's page, multichannel advertising across platforms (e.g., email, Facebook), and the use of trending topics or sponsored tweets.Third, the analysis in this article relies on a brand's followers at a given point in time. Twitter does not provide data on when a user starts or stops following an account. The article's analysis of two different periods highlights the potential for our method to examine how transcendence changes over time. Because most aspects of the data collection and network analyses in this approach can be automated, brand managers could collect followership information at more regular intervals to examine changes in transcendence more frequently. Fourth, while our study relies on validation from two categories, future studies can consider expanding the survey-based validation for broader set of brands across multiple categories. However, for such validation, it is important to consider that certain cross-category cobranding candidates, revealed through the cofollowership patterns on social media, may not always be intuitive to survey respondents, though in hindsight they make sense and work. In conjunction with the brand network results, marketers should apply domain knowledge and managerial judgment to explore different extension and cobranding opportunities that may work best for the brand.Fifth, it is also important for brand managers to consider that Twitter users from around the globe are free to follow any account(s) of a brand (which can include global or country-specific accounts). As the data collection and network analyses can be largely automated, marketers can create custom brand networks to include Twitter brand accounts (country-specific and/or global) that best suit their market of interest. Domain customization can help managers conduct a more targeted network analysis of where the proposed cobranding opportunity may work best. Lastly, although our approach relies on cofollowership patterns to identify cobranding opportunities, we do not investigate the drivers of common followership on Twitter and the extent to which these drivers lead to network overlap between brands. The reasons that users cofollow brands on Twitter are varied and complex, with many unobservable factors possibly at play. Industry research by Nielsen ([29]) indicates that 55% of Twitter users say they follow a brand because they like it, followed by 52% of users who want to keep up-to-date on the latest promotions and offers posted by the brand. There are various other reasons that users cofollow multiple brands on social media; thus, future research could use the brand network described in this article to investigate and better understand the drivers of cofollowership between brands on social media.Overall, this work offers a new approach for researchers and practitioners interested in automatically monitoring cross-category brand connections over time. Network-based methods for brand management are relatively new and present many opportunities for future research. The methods introduced in this article provide a foundation for marketing researchers interested in leveraging implicit brand networks to gain richer insights into consumers and brands. " 38,Leveraging Creativity in Charity Marketing: The Impact of Engaging in Creative Activities on Subsequent Donation Behavior," Charities are constantly looking for new and more effective ways to engage potential donors in order to secure the resources needed to deliver services. The current work demonstrates that creative activities are one way for marketers to meet this challenge. Field and lab studies find that engaging potential donors in creative activities positively influences their donation behaviors (i.e., the likelihood of donation and the monetary amount donated). Importantly, the observed effects are shown to be context independent: they hold even when potential donors engage in creative activities unrelated to the focal cause of the charity (or the charitable organization itself). The findings suggest that engaging in a creative activity enhances the felt autonomy of the participant, thus inducing a positive affective state, which in turn leads to higher donation behaviors. Positive affect is demonstrated to enhance donation behaviors due to perceptions of donation impact and a desire for mood maintenance. However, the identified effects emerge only when one engages in a creative activity—not when the activity is noncreative, or when only the concept of creativity itself is made salient.","Charitable organizations exist to support a wide variety of causes, such as helping malnourished children, caring for the homeless, supporting animal welfare, and meeting environmental concerns, to name a few. The success of these organizations in supporting their causes largely depends on the donations they secure. According to the [58], approximately 1.56 million registered nonprofit organizations exist in the United States, together raising an estimated $390 billion in donations annually. Despite these large numbers, fundraising remains a major challenge for such organizations, with approximately 45% of charities unable to secure the required level of resources needed to deliver their services ([59]).In light of this, it is not surprising that marketers at these organizations seek more effective ways to solicit donations, often utilizing nontraditional approaches and fundraising events (e.g., ice-cream socials, silent auctions, trivia nights) to engage potential donors ([11]). For example, in 2014, the ALS Association invited people around the globe to participate in its ""Ice Bucket Challenge"" to increase awareness of ALS, raising approximately $220 million from individual donors in the process (Holan 2014). Bloodwater.org devised the ""Real Game of Thrones"" campaign, which called on people to participate through Twitter and used a combination of pop culture, humor, and bathroom puns to raise money to build latrines throughout Africa. This creative campaign successfully raised enough money in 24 hours to build 21 latrines in Rwanda. Cookies for Kids, another charitable organization, sponsors creative charity events each year such as cookie swap parties, where participants decorate cookies and swap recipes to raise donations. These fundraising anecdotes suggest that charities are defining new ways of engaging potential donors, while raising questions about which types of activities most effectively enhance donation behaviors.The current work meets this challenge by examining how engaging potential donors in creative activities can positively influence their propensity to donate money to a charitable cause. We argue that engaging in a creative activity induces a positive affective state, which in turn increases both the likelihood and amount of monetary donation made to the charitable organization. While prior work has independently examined links between creativity and positive affect (e.g., [ 9]; [47]), as well as between positive affect and helpfulness (e.g., [ 2]; [ 3]), we provide a deeper understanding of why and how creativity leads to enhanced donation behaviors. Specifically, we show that the link between creativity and positive affect is driven by the sense of autonomy that is induced by engaging in a creative activity (i.e., an attempt to create something novel). Further, by identifying the roles that desire for mood maintenance and perceived donation impact play, we provide insight into why the positive affect resulting from a creative activity leads to enhanced donation behavior.The current research makes several important contributions. From a practical perspective, this research offers a simple and effective way for marketers to improve their donation appeals; it suggests that engaging potential donors in a creative activity enhances subsequent donation behavior. This recommended approach provides a real opportunity for charity marketers to increase the efficacy of their fundraising campaigns. At the theoretical level, the present work advances the marketing and charity literature streams in several ways. First, we demonstrate the positive effect of creative engagement on donation behavior. To our knowledge, no research thus far has examined whether and how engaging in creative activities can impact an individual's subsequent donation behavior toward a charitable organization. Second, we explicate the reasons and conditions that drive the relationship between creativity and donation behavior. We demonstrate that it is the act of actually engaging in a creative activity—rather than simple priming or making the concept of creativity salient—that drives the effect. Further, while prior work has consistently shown that a sense of autonomy can facilitate creativity (e.g., [18]), we demonstrate that engaging in a creative activity also heightens one's sense of autonomy, which in turn induces positive affect. As noted previously, we also highlight that the positive affect experienced during a creative activity bolsters desire for mood maintenance and perceived donation impact, thereby enhancing the likelihood of donation and the monetary amount donated. Finally, we find evidence that the positive effect of engaging in a creative activity on monetary donation is context independent. That is, engaging potential donors in a creative activity not directly related to the charitable cause or organization still has a positive influence on their donation behavior. Thus, the current work not only offers marketers a way to build effective donation campaigns but also provides a deeper theoretical understanding of the relationship between creativity and donation behavior. Conceptual Framework Donation BehaviorResearchers have explored many facets of donation behavior, from the demographic and socioeconomic determinants of donation ([ 6]; [10]; [40]) to the extent to which other factors—such as motivation, psychological characteristics, and social cognition—can affect donation ([34]). In addition, prior research has proposed and examined various marketing strategies and tactics used to increase donations. For example, using public recognition ([73]), taking advantage of price promotions ([78]), designing more attractive appeals ([51]), expressing one's identity ([61]), and using positioning to enhance the effectiveness of the charity ([74]) have all been investigated.More relevant to the current research, recent work has also started to examine the merits of engaging potential donors in different types of activities and tasks before soliciting donations. For example, [62] examined the influence of a storytelling event in the crowdfunding context, finding that direct (vs. indirect) storytelling positively affects customer engagement and donation likelihood. In contrast with more traditional donation requests ([69]), some charities are utilizing physical activities (e.g., walks and runs [[37]], sporting events [[36]], silent auctions [[41]], ice cream socials, trivia nights [[11]]) as precursors to the donation solicitation. Despite the initial academic interest in these tactics, the effectiveness of such approaches has been understudied in the literature, and reporting has shown mixed results. For example, while [37] have argued that positive fundraising outcomes result from physical activity events (e.g., running activities, golf tournaments), [75] did not find a positive relationship between sports activities and charitable event outcomes. The current work aims to add to the literature in this regard by validating the use of activities to increase the likelihood and amount of donation contributions. Specifically, we examine the impact of engaging potential donors in creative activities. Creative Engagement, Autonomy, and Positive AffectActivities involving creation of an output span a continuum ranging from routine tasks, such as simply copying a given design, to highly creative activities, such as creating an original work of art ([18]). Within this context, we argue that the inherent characteristic of creative engagement, which is differentiated from priming or simple salience of creativity and/or creativity-related concepts, is that an individual must engage, physically or mentally, in an activity requiring the production of something novel (i.e., the activity leans to one side of the continuum referenced previously). For example, actively generating an original cookie design would lead to creative engagement, but copying a cookie design or simply being primed by the concept of creativity (e.g., through exposure to creative stimuli) would not.Importantly, we propose that engaging in a creative activity induces positive affect for the creator. In support of this notion, liberal arts literature finds that engaging in creative activities to generate novel outputs (e.g., music composition, visual arts, creative writing) can bring about positive thoughts and feelings ([66]). Relatedly, [ 9] show that engaging in a divergent creative task induces higher levels of positive affect. Results reported in the psychology literature also support these findings. [16], while explicating the construct of flow, interviewed people who engage in creative work on a regular basis (i.e., artists and musicians) and found that these individuals often experience positive affect and happiness when creating something original. [15] confirm these findings in an experimental lab setting and show that engagement in a creative activity induces a state of flow, leading to higher positive affect.Why does engaging in creative activities lead to positive affect? One potential driver of this positive relationship is a heightened sense of autonomy (i.e., having a sense of choice and freedom from external control; [24]; [52]; [63]), attained by engaging in a creative activity.[ 5] By definition, an attempt to generate a creative output requires one to actively recognize remote associations between broad and distant concepts and then combine these loosely connected ideas and concepts in a novel fashion ([20]; [31], [32]; [69]). Such a process requires and encourages one to think freely and make different combinations and choices without being constrained by norms and rules ([ 4]; [30]; [43]). Thus, the process associated with creative generation should manifest a sense of choice and freedom (i.e., autonomy), which we contend induces positive affect.Prior work offers initial support for this proposition. As we have discussed, engaging in a creative activity induces a state of flow, which then leads to higher positive affect ([15]); notably, empirical work has shown the state of flow to be associated with a sense of autonomy ([49]). Similarly, [18] found that being involved in a creative activity can enhance experienced enjoyment, but only when the activity imparts a sense of autonomy. [47] conducted a daily-diary study following the routine of 1,042 hobby musicians and found that the participants reported higher positive affect on the days they engaged in music composition and performance. Importantly, the authors found that this relationship was driven by satisfaction of one's needs for autonomy. Finally, [46] found that creative generation, such as the production of visual art, significantly reduced cortisol levels (a biomarker and proxy measure of stress in humans) and increased feelings of relaxation, pleasantness, and enjoyment. Their work shows that such art making is associated with the experience of being free from constraints (i.e., the sense of autonomy).Given this discussion, we argue that engagement in creative activities heightens one's sense of autonomy, which in turn leads to positive affect. We further propose that the positive affect induced by participation in a creatively engaging activity will lead to enhanced donation behavior. We elaborate on this prediction in the following subsection. Positive Affect and Donation BehaviorFindings reported in the extant literature offer compelling evidence that being in a positive affective state enhances donation behavior ([ 1]; [17]; [26]; [42]; [44]; [60]). While prior work has consistently demonstrated a positive relationship between positive affect and donation behavior, it offers disparate explanations for this relationship ([ 7]). Indeed, we recognize that the relationship between positive affect and donation behavior is likely to be multiply determined, and we therefore identify three mechanisms that are most relevant to the context of creative engagement in question.Perhaps the most common explanation for the clear link between positive affect and helping behavior derives from the mood maintenance model. This line of reasoning proposes that people tend to maintain positive mood states ([ 7]; [33]). Thus, individuals tend to help more when in a positive affective state, because doing so enables them to prolong said state ([12]; [45]). In the context of our work, this suggests that the positive affect realized by participating in a creative activity can best be maintained when a subsequent behavior, such as helping others through enhanced donation behavior, also fosters positive feelings ([ 7]).A second potential mechanism derives from the social aspects associated with positive affect. Indeed, it has been argued that being in a positive affective state can directly influence one's perceived social connectedness ([38]; [39]). As such, the positive affect defined by participation in a creative activity is likely to enhance the value of creating and maintaining social connections ([13]; [25]). Further, valuing social connections has been shown to enhance feelings of care and concern toward others ([ 8]), which should subsequently enhance the donation behaviors of the individual.Finally, prior work reports that positive affect can also boost self-efficacy and/or the perceived impact of one's actions ([ 5]; [64]). That is, experiencing positive affect can lead to the belief that one's actions are more efficacious, thus creating a heightened expectancy of positive outcomes ([53]). Thus, we contend that positive affect can enhance the perceived impact of one's potential donation and, in turn, raise the likelihood and amount of one's donation behavior ([14]). In our empirical work, we explicitly test the proposed chain of effects (i.e., engagement in a creative activity → autonomy → positive affect → enhanced donation behaviors), and the three aforementioned potential mechanisms underlying the impact of positive affect on donation behavior. Summarizing our arguments, we hypothesize the following: H1: Engaging in a creative activity (vs. an activity that does not provide an opportunity for novel creation) leads to enhanced donation behaviors (i.e., the likelihood of donation and the monetary amount donated). H2a: The relationship between engaging in a creative activity and donation behavior is serially mediated by autonomy and positive affect. H2b: The influence of positive affect on donation behavior is driven by (a) mood maintenance, (b) social connection, and/or (c) perceived donation impact. Overview of StudiesWe utilized a combination of field studies and controlled lab experiments to test our hypotheses. First, we conducted a pilot study in collaboration with a nonprofit organization as an initial test of our focal hypothesis and found support for the prediction that engaging in a creative activity enhances donation behavior (H1). Study 1, conducted in a controlled lab setting, replicated the initial pilot study findings, thus reconfirming support for H1. Study 2, a quasi-field experiment, shows that engaging in a creative activity increases both the likelihood and amount of monetary donations, whereas simply priming the concept of creativity does not (H1). Study 3 tested the full serial mediation prediction (H2a) by demonstrating that creative engagement induces a sense of autonomy, which in turn heightens positive affect, leading to higher donation behavior. Study 4 tested H2b, showing that the path from positive affect to donation behavior is indeed multiply determined. In every study, we report all experimental conditions and measures as collected and disclose any eliminated data points when applicable. The sample size was predetermined for each study based on current experimental norms but varied within an acceptable range depending on actual participant sign-ups. Study 3, a supplementary study (follow-up to Study 3 reported in the Web Appendix), and Study 4 were preregistered on aspredicted.org (see respective studies for details). Pilot Study: Creativity and Monetary Donations—A Field ExperimentTo gain an initial understanding of whether engaging in a creative (as compared with neutral) activity enhances monetary donation, we collaborated with a registered U.S. nonprofit organization operating an animal shelter in a small city in the Southwestern United States (population 46,000 according to 2010 U.S. Census data). Every year, employees of this charity produce T-shirt designs that are printed and used as giveaways or sold in fundraising activities. To test our focal hypothesis, the charity agreed to open the T-shirt design activity to the public and use it as a fundraising event. The T-shirt design campaign was launched by the charity via its social media platform, inviting members of the public (i.e., potential donors) to create T-shirt designs as part of a donation appeal. The charity had set two overarching guidelines for this T-shirt design campaign: ( 1) the submitted designs were to follow the theme of ""Rescue 2020"" and ( 2) the charity's logo had to be part of the design. The charity managed the entire event.Relevant to our prediction (H1), we manipulated the opportunity to be creative (vs. not) within the T-shirt design campaign. While participants in both conditions had to develop a T-shirt design reflecting the charity's yearly theme and including its logo, those in the creativity condition were invited to develop an innovative T-shirt design and were explicitly instructed to be creative while doing so. Those in the neutral condition were not specifically instructed to be creative. Once participants submitted their designs, the charity presented them with a donation appeal that included a link to the donation page. At the end of the campaign period, the charity forwarded us the designs along with the corresponding donation amounts, having removed donors' identifying information (for additional methodological details, see Web Appendix A.1).To examine the relationship between creativity and donation behavior, we first coded the participants who did not donate as 0 and those who donated (any amount greater than zero) as 1. We then conducted a binary logistic regression analysis testing the effect of engaging in a creative T-shirt design activity on donation rate (i.e., the percentage of participants who donated to the charity). We found that a significantly higher percentage of people in the creativity condition (34.48%) donated, as compared with the percentage of people in the neutral condition (12.20%; χ2 = 4.97, p = .026). Next, we examined the effect of creative engagement on the amount of money that was donated. A Shapiro–Wilk test ([65]) indicated that the donation amount data was not normally distributed (p <.001). Thus, in accordance with prior research ([57]; [61]), we used a nonparametric Mann–Whitney U test for the analysis. We found that the average donation amount made by participants in the creativity condition (M = 7.07, SD = 13.06) was significantly higher than that of participants in the neutral condition (M = 1.10, SD = 3.26; U = 739.50, p = .016; for additional results, see Web Appendix A.2).By collaborating with a registered charity and assessing actual donation behaviors, we found initial evidence showing that including a creative activity as part of a donation appeal can be an effective approach to enhance donation behaviors (i.e., both higher donation rates and amounts). Interestingly, one could argue that the creativity of the generated output (i.e., the T-shirt design) may also have impacted the donation behavior. To test this alternative explanation, we asked two trained research assistants (employed within the domains of creativity and advertising, respectively) to rate each T-shirt design on its creativity (1 = ""not at all creative,"" and 7 = ""very creative""). Both raters were blind to the conditions and hypothesis. Validating our manipulation, a one-way analysis of variance (ANOVA) showed that the designs produced in the creative condition (M = 3.62, SD = .80) were rated as significantly more creative than those in the neutral condition (M = 3.05, SD = .98; F( 1, 68) = 6.72, p = .01). However, we did not observe a significant relationship between rated creativity of the generated designs and the donation rate (B = .04, t < 1) or the donation amount (B = .83, t < 1). We conducted a similar analysis using only the creative condition, where natural variability in output creativity may occur (even though everyone was instructed to be creative). Again, we did not find a significant relationship between the creativity of the generated designs and the donation rate (B = −.15, t = −1.34, p = .19) or the donation amount (B = −3.07, t < 1). Importantly, our conceptualization argues that it is the act of engaging in a creativity activity that leads to enhanced donation behavior, not the level of creative output achieved. Subsequent studies replicate this finding, consistently demonstrating that creative engagement itself, rather than the creativity of the generated outcome, positively impacts donation behavior (for brevity, these findings are reported in the respective Web Appendices).This pilot study provided initial evidence of the proposed effect, but as a real-life field study conducted in collaboration with a third party, it is not without limitations (dependence on the charity's social media platform for sampling, the messaging guidelines set by the charity, etc.; for discussion, see Web Appendix A.3). As such, our first study aimed to replicate these initial findings observed in the field in a more controlled lab setting (i.e., provide a more robust test of H1). Study 1: Creativity and Monetary DonationsStudy 1 used a donation context inspired and adapted from a real-life social enterprise known as Elephant Parade. This organization invites everyday consumers to create/paint their own elephant toy using an ""Artbox Kit"" (containing a small white clay elephant and a variety of colors) in return for a monetary donation. The proceeds are subsequently used for elephant welfare and conservation projects worldwide. MethodEighty-nine undergraduate students (49 women; Mage = 20.04 years, SD = 1.27 years) at a large North American university completed this study in exchange for course credit. To begin, participants were checked in and assigned to a designated computer desk, each of which was equipped with a small donation box (see Web Appendix B.1) and a white envelope containing $2 in quarters (i.e., eight quarters). The donation box was labeled with an Elephant Parade sticker and had a slit on the top. Four quarters were left in each donation box, creating the impression that the study administrator would not be able to tell if the participant donated or not, thus reducing any demand effects and obligation to donate. Participants were told that, in addition to the course credit, they would receive $2 (in an envelope on their desks) as a token of appreciation for their participation.The experiment adopted a one-way design in which participants were assigned to complete either a creative or neutral activity, randomized by session (i.e., we ran only one condition per session). A drawing activity (inspired by Elephant Parade's clay elephant painting) was used to induce the focal manipulation. Participants were told that the researchers wanted to put their minds at ease before the study commenced and would therefore like them to engage in a coloring activity. All participants were given a sheet of paper with a picture of an elephant (see Web Appendix B.2) and asked to color it. Those in the creative condition received a box of Crayola markers in ten different colors and were told to be as creative as possible while coloring and decorating their elephant pictures. They were also told to use any number and variety of colors they liked for the task. In contrast, those in the neutral condition received gray crayon markers only, and were told to simply color the elephant picture. In both conditions, participants were asked to spend no more than five minutes on the coloring activity. The elephant coloring task, though based on a real-life activity (i.e., Elephant Parade donation protocol), mimics a widely used creativity task in the literature: the alien task ([72]). In these types of activities, creative thinking encourages people to violate standard characteristics of a stereotypical object (e.g., an elephant, as in our study; [48]; [50]). A stereotypical elephant picture would be colored gray, whereas a nonstereotypical elephant picture would be multicolored.Next, participants were presented with the donation opportunity and informed that the researchers were helping raise money for the nonprofit organization Elephant Parade. Participants read a short description and donation appeal from Elephant Parade (see Web Appendix B.3), and were asked if they would like to contribute; they could donate any amount of the participation money (eight quarters) they wished, and put it in the donation box. The number of quarters each participant donated served as the key dependent variable. Finally, all participants provided their demographic information (age and gender) and were debriefed before being dismissed. (Gender and age were captured in all studies. However, no effects were observed for these variables in either this or any other study. For the sake of brevity, we do not discuss them further.) After each session, the research assistants removed the quarters from the donation boxes and recorded the number of donated quarters (i.e., the total number of quarters in the box minus the four quarters initially placed in each donation box). Results Preliminary analysesThe elephant designs were rated on creativity (1 = ""not at all creative,"" and 7 = ""very creative"") by a research assistant who was blind to the conditions and hypothesis (for sample designs, see Web Appendix B.4). A one-way ANOVA confirmed that the designs produced in the creative condition (M = 4.24, SD = 1.61) were significantly more creative than those produced in the neutral condition (M = 1.51, SD = .95; F( 1, 87) = 97.41, p <.001). Donation behaviorsFirst, we explored whether there was any difference between the two conditions on the donation rate (i.e., the percentage of participants who donated to the Elephant Parade foundation) and found that a significantly higher percentage of participants (80.95%) in the creative condition—as compared with those in the neutral condition (55.32%)—donated (χ2 = 6.83, p = .009). Next, we analyzed the effect of activity type on donation amount, which was assessed by the number of quarters donated to the Elephant Parade after completing either the creative or neutral activity. The donation data did not meet the normal distribution criteria (Shapiro–Wilk test: p <.001; [65]); therefore, a nonparametric Mann–Whitney U test was again used for the analysis. We found that those who completed the creative activity (M = 4.50 quarters, SD = 3.19) donated a significantly higher number of quarters than those who completed the neutral activity (M = 2.98 quarters, SD = 3.54; U = 720, p = .022). DiscussionThe obtained results provide support for our focal hypothesis (H1), namely, that engaging in a creative activity enhances donation behavior, in terms of both the likelihood of donation (i.e., donation rate) and the donation amount. The study utilized a creative activity adapted from a real-life charity and assessed donation behavior through real monetary donations. It demonstrated that engaging potential donors in creative activities, before soliciting them for donations, can be an effective way to enhance donation behavior.One potential criticism of this study could be the different number of colors provided in the creative versus neutral conditions. However, this procedure was necessary to manipulate creativity within the context of the study. Offering a variety of colors provides participants with an opportunity for creativity, that is, to think outside the box and beyond the stereotypical characteristics of an elephant (i.e., all gray). The sole use of gray crayon markers in the neutral condition, in contrast, conforms to the stereotypical characteristics of an elephant and curtails creative opportunity. To address this potential limitation, in future studies we adopt contexts in which we can provide the same materials to participants in both conditions.In both the pilot study and Study 1, the creative activity was directly related to the charitable cause, thereby raising a question about the generalizability of the effect—that is, whether the observed effect is domain specific or whether it holds when the creative activity is independent of the donation context. We explore this possibility in Study 2. In addition, our studies do not delineate whether the obtained results were observed because participants engaged in a creative activity (as hypothesized), or simply because the concept of creativity was salient for the participants in the creativity condition. In other words, is it necessary to actually engage in a creative activity, or can mere exposure to the concept of creativity also enhance donation behaviors? Prior research has shown that priming creativity (making creativity salient without engagement) can influence cognitive processing, thereby affecting people's propensity to engage in dishonest behaviors ([27]). We examine this question in the next study. Study 2: Creative Stimuli Versus Creative ActivityStudy 2 was aimed to discern whether engagement in a creative activity is needed to obtain the identified effects, or rather, if exposure to a simple creative prime would suffice. To this end, we added another focal condition to the experimental design used in Study 1: this time participants were exposed to creative stimuli only, with no opportunity to participate in a creative activity. Interestingly, this condition mimics the default strategy of many charitable organizations, in which potential donors are presented solely with a donation appeal (without an opportunity for active engagement). In addition, to test the context-independent nature of the effect, the creativity activity was kept independent from the donation context (i.e., the charitable cause). Finally, we conducted the study in a real-life setting; we followed a format used by baked goods company C. Krueger's, which hosts a holiday charity event wherein customers are invited to decorate cookies and make purchases. For our study, two booths were set up in the lobby of a university building with high foot traffic, featuring large signs advertising a cookie decoration event sponsored by the charity ChildHelp. Passersby were invited to participate in the event and decorate a cookie before being solicited for a monetary donation. MethodOne hundred seventy adults (82 women; Mage = 21.09 years, SD = 2.47 years) agreed to participate in the event and were assigned to one of the three treatment conditions: creative engagement, creative exposure without engagement, or neutral engagement. The conditions were randomized and rotated by the hour. Once passersby agreed to participate in the event, they were told they would receive $2 as a token of appreciation for participating in the event. They were given a white envelope containing eight quarters and asked to sign a form indicating receipt of the money. The signing process was necessary for participants to feel ownership of the money they had earned, before being solicited for donations later in the study. Prior research has shown that signing one's name increases this sense of ownership ([22]; [68]).Next, one of the ""staff members"" guided individual participants to a table bearing a plain cookie on a paper plate, four different icing colors, and a spatula. They were also handed an event participation instruction sheet, which served as our key manipulation. Each instruction sheet had the ChildHelp Foundation logo at the top, with ""ChildHelp Foundation Annual Charity Event"" printed underneath (see Web Appendix C.1). The task manipulation for the two engagement conditions (i.e., cookie decoration) was adapted from [18]. In the creative engagement condition, participants were told that this was an annual charity event hosted by the ChildHelp Foundation and, as part of the event, we wanted them to decorate their cookie in the most creative manner possible using the provided materials. Those in the neutral engagement condition were given a picture of a routinely (i.e., noncreatively) decorated cookie (see Web Appendix C.2) and asked to ice the cookie as shown in the picture, using the provided materials. Those in the creative condition had the freedom to use their imagination and creativity to come up with a novel cookie design, thereby promoting creative engagement. However, in the neutral condition, participants were simply asked to copy the noncreative cookie as depicted, negating any potential creative engagement process. In the creative exposure (without engagement) condition, in keeping with prior research showing that exposure to creative images can make the concept of creativity accessible ([76]), we simply showed participants three creative cookie designs (see Web Appendix C.3) and asked them to choose the most creative one.To assess whether our manipulation made the concept of creativity salient, we conducted a separate online study. In this study, participants were randomly assigned to complete one of the three treatment condition tasks used in the main study (creative engagement, neutral engagement, or creative exposure without engagement). They were then presented with two types of measures that captured the salience of creativity implicitly and explicitly. The obtained results showed that, as anticipated, the concept of creativity was equally salient for both the creative engagement and creative exposure conditions, and both were significantly higher than the neutral condition (for study details and complete results, see Web Appendix C.4).Next, in the main study, all participants were given a manila envelope with a survey featuring a donation appeal and some questions about the cookie event. Each envelope was marked with a unique identification number to enable us to match participants' survey responses, donation amounts, and their assigned condition. All participants were presented with a donation appeal from the ChildHelp Foundation: a nonsectarian, nonpolitical, registered charity dedicated to helping children living in distress in North America and overseas. Furthermore, participants were told that if they decided to contribute, they could put the quarters they wanted to donate in the manila envelope and leave it in the box beside their table. Lastly, to gain initial insights into the underlying process, we captured exploratory measures of participants' positive affective state and their perceived donation impact (for details, see Web Appendix C.5). At the end of the study, the participants in the two engagement conditions were invited to take their cookie with them, while those in the creative exposure without engagement condition were given a cookie at the end of the study (for consistency with the other two conditions). ResultsWe first examined the donation rate by calculating the percentage of participants who donated in each condition. A logistic regression revealed a significant difference in the donation rates across the three conditions (χ2( 2) = 12.26, p = .002). A significantly higher percentage of participants in the creative engagement condition (81.03%) donated, compared with both those in the creative exposure (without engagement) condition (50.88%; χ2( 1) = 11.01, p = .001) and those in the neutral condition (61.82%; χ2( 1) = 4.98, p = .026). We observed no difference between the latter two conditions (p = .244). Next, we assessed the donation amount, with the number of donated quarters serving as our key dependent variable. A Shapiro–Wilk test ([65]) indicated that our data were not normally distributed (p <.001); therefore, we used the nonparametric Kruskal–Wallis test for the analysis. The obtained results revealed a significant overall main effect of the activity type on monetary donation (H( 2) = 9.8, p = .007). Pairwise comparisons showed that those in the creative engagement condition (M = 6.10 quarters, SD = 3.30) donated significantly more quarters than both those who were in the creative exposure (without engagement) (M = 4.04 quarters, SD = 3.97, p = .003) and neutral engagement (M = 4.49 quarters, SD = 3.85, p = .021) conditions. There was no difference between the latter two conditions (p = .52). DiscussionIn this study, we created a charity event in which individuals participated in different activities before receiving a donation solicitation. In line with our predictions, we found that participating in an activity that enabled creative engagement (i.e., creatively decorating a cookie) enhanced donation behavior (as compared with those who either reproduced a routine cookie design or were merely exposed to creative cookie designs). Importantly, the obtained results showed that donation behavior is only enhanced when participants actually engage in a creative activity, not simply when the notion of creativity is made salient. Further, the creative activity utilized in this study was independent of the charity cause, thereby demonstrating the context-independent nature of the effect.Our findings, so far, provide consistent evidence for the hypothesized relationship between creative engagement and donation behavior. In the following studies, we extend our examination to understand the underlying process through which creative activity and higher donation behavior are connected. In particular, we examine the mediating role of positive affect in this relationship. In addition, in Study 3 we also test the role of autonomy as a driver of creative engagement's impact on positive affect, which consequently influences donation behavior. Study 4 then explores why positive affect has such a significant impact on donation behavior. Finally, given the null findings in the creative exposure condition (on donation behavior), we dropped this condition from subsequent studies. Study 3: Testing the Role of Autonomy and Positive AffectStudy 3 was conducted to fully test H2a and identify the underlying process through which creative engagement impacts donation behavior. In particular, we tested our prediction that engaging in a creative activity heightens one's sense of autonomy, which in turn induces positive affect, leading to higher donation behavior. This study was preregistered on aspredicted.org (https://aspredicted.org/blind.php?x=ag5ci3). MethodTwo hundred adults (117 women; Mage = 32.76 years, SD = 11.51 years) recruited from the online platform Prolific completed this study in exchange for a small monetary compensation. At the outset, participants were told that in addition to their regular compensation, they would also receive $1 as a thank-you bonus for completing the study, with an opportunity to spend this money later if they chose to. They were further informed that the study was being conducted in collaboration with the charitable organization ChildHelp Foundation and were provided with information about the organization (see Web Appendix D.1). Next, all participants were told that the ChildHelp Foundation runs an annual charitable event, wherein individuals are invited to participate in various tasks. However, given the COVID-19 pandemic, this year's event would be virtual, and the organization needed help planning the function. Thus, the organization was inviting them to participate in this study as if they were actual donors participating in the event.The activity type manipulation used an idea generation task, mimicking the ""Think outside the cereal box"" campaign Kellogg launched several years ago. In the creative condition, participants were told that as part of its annual charity event, the ChildHelp Foundation was inviting them to ""think outside the cereal box"" and generate a fun and creative way to use Froot Loops cereal, besides eating it for breakfast. Further, participants were told to be as creative as possible and use their imagination to generate an innovative way to use Froot Loops cereal. In the neutral condition, participants were asked to ""think about the cereal"" and share a traditional way of how they eat the Froot Loops cereal (for detailed instructions, see Web Appendix D.2).Once participants completed the Froot Loops task, we assessed their donation behavior, sense of autonomy, and affective state. To capture participants' donation behavior, they were told that the ChildHelp Foundation was seeking donations, and they could help by donating part or all of their $1 bonus (in multiples of $.10) to the charity. All participants were then provided with a scale from $0 to $1 in increments of $.10 to indicate their donation amounts.Sense of autonomy was measured by adapting established measures defined in the literature (i.e., [18]; [56]). Specifically, participants were asked to indicate how they felt while generating their ideas during the Froot Loops task: ( 1) ""To what extent did you feel you had autonomy in generating your ideas during the Froot Loops task?,"" ( 2) ""To what extent did you feel you had freedom in coming up with your ideas for the Froot Loops task?,"" ( 3) ""How free did you feel in generating your ideas for the Froot Loops task?,"" and ( 4) ""How much did you feel you were able to express yourself when generating your ideas for the Froot Loops task?"" (1 = ""not at all,"" and 7 = ""very much""). Next, to measure positive affect, they were asked to think back to the Froot Loops activity and indicate how they felt during this activity on 11 items adapted from [19] and [18]. Specifically, all participants reported how they felt while completing the Froot Loops activity: ( 1) 1 = ""very negative,"" and 7 = ""very positive""; ( 2) 1 = ""very unpleasant,"" and 7 = ""very pleasant""; ( 3) 1 = ""not at all nice,"" and 7 = ""very nice""; and ( 4) 1 = ""very bad,"" and 7 = ""very good."" This was followed by seven items anchored with 1 = ""not at all,"" and 7 = ""very much,"" asking ( 5) how positive they felt during the Froot Loops activity, ( 6) the extent to which they enjoyed the Froot Loops activity, ( 7) the extent to which they had a good time during the Froot Loops activity, ( 8) how much fun the Froot Loops activity was, ( 9) how satisfied they felt during the Froot Loops activity, (10) how pleasurable the Froot Loops activity was, and (11) how exciting the Froot Loops activity was. Results Preliminary analysesA trained research assistant blind to hypothesis and condition rated the creativity of the generated Froot Loops ideas. As we expected, a main effect of creativity emerged such that those in the creative condition (M = 3.89, SD = 1.29) generated more creative ideas than those in the neutral condition (M = 1.24, SD = .81; F( 1, 198) = 301.96, p <.001, Cohen's d = 2.46). Donation behaviorsWe first examined the effect of engaging in the creative (vs. neutral) activity on the donation rate. A binary logistic regression revealed that a significantly higher percentage of participants in the creative condition (63.37%) donated money, compared with the percentage of participants in the neutral condition (45.45%; χ2( 1) = 6.50, p = .01). Next, we examined the difference between the creative and neutral conditions' donation amounts. As in previous studies, a Shapiro–Wilk test ([65]) indicated that the data were not normally distributed (p <.001); thus, we used the nonparametric Mann–Whitney U test for the analysis. Replicating the results from the previous studies, those who engaged in the creative (M = 41.49¢, SD = 41.60¢) versus the neutral (M = 27.07¢, SD = 37.04¢) activity donated significantly more money (U = 6,034.50, p = .007). Process measuresFactor analysis showed that all four items used to capture participants' sense of autonomy loaded onto the same factor; therefore, we averaged them to create a sense of autonomy index (α = .92). A one-way ANOVA revealed that those in the creative condition (M = 6.05, SD = 1.02) reported a significantly higher sense of autonomy than those in the neutral condition (M = 4.94, SD = 1.73; F( 1, 198) = 30.61, p <.001, Cohen's d = .78). In addition, factor analysis showed that the 11 items used to capture participants' positive affective state loaded onto the same factor, and we therefore averaged them to create a positive affect index (α = .96). A one-way ANOVA revealed that those in the creative condition (M = 5.24, SD = 1.24) reported a significantly higher positive affective state than those in the neutral condition (M = 4.52, SD = 1.31; F( 1, 198) = 16.15, p <.001, Cohen's d = .56). Mediation analysisTo test the potential underlying process paths, we first examined the mediation effect of positive affect on the creative engagement/donation rate relationship. A bias-corrected bootstrap confidence interval obtained by resampling the data 10,000 times (with activity type as the independent variable, positive affect as the mediator, and the donation rate as the dependent variable) did not include zero (β = .31, SE = .12, bias-corrected 95% confidence interval [CI] = [.11,.59]), indicating a significant indirect (i.e., mediation) effect. Next, we conducted a serial mediation analysis to understand the role of autonomy in this relationship. Serial mediation (Model 6, [35]) conducted with activity type as the independent variable, sense of autonomy and positive affect as the serial mediators (in that order), and the donation rate as the dependent variable together revealed the presence of a significant indirect effect (β = .21, SE = .09, bias-corrected 95% CI = [.07,.41]).We also conducted the same mediation analyses for the donation amount. A bias-corrected bootstrap confidence interval obtained by resampling the data 10,000 times (with activity type as the independent variable, positive affect as the mediator, and the donation amount as the dependent variable) did not include zero (β = 4.71, SE = 1.99, bias-corrected 95% CI = [1.32, 9.02]), indicating a significant indirect (i.e., mediation) effect. Next, we conducted a serial mediation analyses to understand the role of sense of autonomy. A serial mediation (Model 6, [35]) conducted with activity type as the independent variable, sense of autonomy and positive affect as the serial mediators (in that order), and the donation amount as the dependent variable together revealed the presence of a significant indirect effect (β = 2.99, SE = 1.48, bias-corrected 95% CI = [.29, 6.10]). DiscussionStudy 3 results replicated the findings from the previous studies and showed that engaging in a creative (as compared with neutral) activity enhances donation behaviors (H1). Further, the findings from this study highlighted the underlying processes through which creative engagement affects monetary donation. As we hypothesized, creative (vs. neutral) engagement induces a higher positive affect, which in turn leads to enhanced donation behavior. Importantly, we found that sense of autonomy drives the relationship between creative engagement and positive affect (H2a is fully supported). To further confirm the role of autonomy in this relationship, we conducted a supplementary study in which we directly manipulated the sense of autonomy felt by the participant. Here, we showed that when felt autonomy is mitigated, the positive effect of creative engagement on positive affect (and, in turn, the donation behavior) is attenuated (for the details of this supplementary study, see Web Appendix E).In the next study, we further explicate the underlying process through which creative engagement impacts donation behavior by examining the pathways through which positive affect leads to higher donation behavior. Study 4: Exploring Why Positive Affect Impacts Donation BehaviorWe conducted Study 4 to test H2b and provide additional insight into the underlying process through which creative engagement impacts donation behavior. In particular, we assessed the possible role of mood maintenance, social connection, and perceived donation impact in driving positive affect's influence on donation behavior outcomes. This study was also preregistered on aspredicted.org (https://aspredicted.org/blind.php?x=j8rj2w). MethodTwo hundred adults (109 women; Mage = 34.84 years, SD = 12.91 years) recruited from the online platform Prolific completed this study in exchange for a small monetary compensation. At the outset, participants were told that in addition to their regular compensation, they would also receive $1 as a thank-you bonus for completing the study, with an opportunity to spend this money later if they chose to. They were further informed that the study was being conducted in collaboration with the charitable organization Healthier Tomorrow and were provided with information about the organization (see Web Appendix F.1). Next, all participants were told that Healthier Tomorrow runs an annual charitable event, wherein individuals are invited to participate in various tasks. However, given the COVID-19 pandemic, this year's event was going to be virtual, and the organization needed help planning the function. Thus, the organization wanted to invite them to participate in this study as if they were actual donors participating in the event.Next, participants were randomly presented with either the creative or neutral version of the event and asked to create (reproduce) a T-shirt design. Those in the creative condition were specifically asked to design an innovative T-shirt and be as creative as possible (for detailed instructions and the sample designs produced by participants, see Web Appendix F.2). In the neutral condition, participants were simply provided with a generic T-shirt design and asked to reproduce it, thus negating any potential creative engagement (for detailed instruction and the T-shirt design provided to the participants, see Web Appendix F.3). Participants were then directed to the T-shirt customization website (customink.com) to complete the design activity. Once participants had finished creating (reproducing) their designs they were asked to save them with a unique ID provided in the survey and then use the save/share function in the T-shirt customization website to email their design to a designated email address created for the study.Once participants completed and submitted information about their designs, we assessed their donation behavior, affective state, mood maintenance, social connection, and perceived donation impact. To capture participants' donation behavior, we told them that Healthier Tomorrow was seeking donations, and they could contribute by donating part or all of their $1 bonus (in multiples of $.10) to the charity. All participants were then provided with a scale from $0 to $1 in increments of $.10 to indicate their donation amounts.To assess participants' positive affective state, we asked them to think back to the T-shirt design task and indicate how they felt during this activity, on the same 11 items used in Study 3 (adapted from [19]] and [18]]). Participants responded to a mood-maintenance measure adapted from [23], where they were asked to think about their donation decision and indicate their agreement with the following statements on seven-point scales (1 = ""not at all,"" and 7 = ""very much""): ""I thought ..."" ( 1) ""I would feel good about myself if I donate,"" ( 2) ""donating will make me feel good,"" and ( 3) ""if I donate it would be a personally rewarding experience."" To measure social connection, we adapted items from [ 8] to suit the context of our study. Participants responded to the following items on seven-point scales (1 = ""not at all,"" and 7 = ""very much""): ""To what extent did you feel ..."" ( 1) ""closer to Healthier Tomorrow,"" ( 2) ""connected to Healthier Tomorrow,"" and ( 3) ""that completing the design task affected the way you think about the relationship with Healthier Tomorrow."" We measured participants' perceived donation impact by means of three items adapted from [14] and [67]. These items specifically asked the participants how much they thought their donation could ( 1) make a positive difference, ( 2) be valuable, and ( 3) do a lot of good (1 = ""not at all,"" and 7 = ""very much""). Results Preliminary analysisTen participants did not complete the T-shirt design activity and were excluded from the analysis (including these participants in the analysis does not change the significance or pattern of results; see Web Appendix F.4). Donation behaviorsWe first examined the effect of engaging in the creative (vs. neutral) activity on the donation rate. A binary logistic regression revealed that a significantly higher percentage of participants in the creative condition (47.87%) donated money, compared with the percentage of participants in the neutral condition (27.08%; χ2( 1) = 8.85, p = .003). Next, we examined the difference in donation amounts between the creative and neutral conditions. As in previous studies, a Shapiro–Wilk test ([65]) indicated that the data were not normally distributed (p <.001); thus, we used the nonparametric Mann–Whitney U test for the analysis. In a replication of the results from the previous studies, those who engaged in the creative (M = 22.34¢, SD = 31.81¢) versus the neutral (M = 11.25¢, SD = 24.63¢) activity donated significantly more money (U = 5,517, p = .002). Process measureFactor analysis showed that all 11 items used to capture participants' positive affective state loaded onto the same factor, and we therefore averaged them to create a positive affect index (α = .97). A one-way ANOVA revealed that those in the creative condition (M = 5.32, SD = 1.32) reported a significantly higher positive affective state than those in the neutral condition (M = 4.72, SD = 1.40; F( 1, 188) = 9.26, p = .003, Cohen's d = .45). Mediation analysisAs an initial step, we examined the mediation effect of positive affect on the creative engagement/donation rate relationship. A bias-corrected bootstrap confidence interval obtained by resampling the data 10,000 times (with activity type as the independent variable, positive affect as the mediator, and donation rate as the dependent variable) did not include zero (β = .19, SE = .11, bias-corrected 95% CI = [.03,.48]), indicating a presence of a significant indirect (i.e., mediation) effect.Next, we examined the pathways through which positive affect, as induced by engaging in creative activity, impacts donation rate. We tested a sequential-parallel mediation model with creative engagement as the independent variable, positive affect as the first mediator, three factors (mood maintenance, perceived donation impact, and social connection) as a second set of mediators in parallel, and donation rate as the dependent variable (see Figure 1) using structural equation modeling (for statistics for each path in the model, see Table 1). This model is very similar to a serial mediation model; however, no order is assumed among the second set of mediators (i.e., mood maintenance, perceived donation impact, and social connection) ([21]). We used bootstrapping procedures to compute 95% CIs by generating 10,000 resamples. The results indicated significant serial indirect effects through positive affect and mood maintenance (β = .031, SE = .014, bias-corrected 95% CI = [.011,.068], p = .001) and positive affect and perceived donation impact (β = .031, SE = .014, bias-corrected 95% CI = [.010,.067], p = .001). However, the serial indirect effect of positive affect and social connection on the creative engagement and donation rate relationship was not significant (β = .005, SE = .012, bias-corrected 95% CI = [−.015,.035], p = .57). Interestingly, with positive affect in the model, creative engagement did not directly impact any of the second set of three mediators, thereby demonstrating the importance of positive affect in the conceptualization. Positive affect positively influenced all three potential mediators, but only mood maintenance and perceived donation impact significantly impacted donation rate. Thus, the positive affect experienced during a creative activity bolstered the desire for mood maintenance and the perceived donation impact, which in turn enhanced donation behaviors.Graph: Figure 1. Sequential-Parallel Mediation Model (Study 4).GraphTable 1. Sequential-Parallel Mediation Model (Study 4). 1 Notes: Path analysis assessing the effect of creative (vs. noncreative) engagement on donation rate/amount through positive affect, mood maintenance, perceived donation impact, and social connection (estimates and 95% CIs for individual paths).A similar analysis was conducted for donation amount. First, a bias-corrected bootstrap confidence interval obtained by resampling the data 10,000 times (with activity type as the independent variable, positive affect as the mediator, and the donation amount as the dependent variable) did not include zero (β = 1.80, SE = 1.13, bias-corrected 95% CI = [.04, 4.44]), indicating a significant indirect (i.e., mediation) effect of positive affect. Next, to examine the pathways through which positive affect impacts donation, we used structural equation modeling to test the hypothesized process model, with donation amount as the dependent variable (for statistics for each path in the model, see Table 1). Ninety-five percent confidence intervals obtained by generating 10,000 bootstrap resamples indicated significant serial indirect effects through positive affect and mood maintenance (β = 1.51, SE = .78, bias-corrected 95% CI = [.40, 3.72], p = .004) and positive affect and perceived donation impact (β = 1.60, SE = .76, bias-corrected 95% CI = [.53, 3.65], p = .001). However, similar to what we observed for the donation rate, the serial indirect effect of creative engagement through positive affect and social connection on donation amount was not significant (β = .33, SE = .81, bias-corrected 95% CI = [−1.06, 2.26], p = .58). DiscussionThe Study 4 results replicated the findings from the previous studies and showed that engaging in a creative (vs. neutral) activity induces positive affect, which in turn enhances donation behaviors. Importantly, this study further examined the underlying process, providing understanding of how positive affect influences donation behavior. We found that the path from positive affect to donation behavior was multiply determined. Indeed, perceived donation impact and mood maintenance were both shown to be drivers of the affect/donation relationship. Interestingly, we did not find evidence for social connection as a mechanism that triggered the identified effects. Thus, it appears that attributions flowing from the positive experience of creative engagement are more at play in defining enhanced donation behavior, and efforts to maintain positivity also spill over into the donation outcomes. Future research should continue to explore these identified mechanisms and discern within which charitable contexts they are most applicable and effective. General DiscussionThe current research examined the relationship between creativity and donation behavior. A pilot study and four subsequent experiments demonstrated that engaging in a creative activity induces a sense of autonomy, which leads to a positive affective state, which in turn results in enhanced donation behaviors (i.e., the likelihood of donation and the monetary amount donated; for a summary of all study results, see Table 2). We further showed that the positive affect experienced by the creator leads to enhanced donation behavior, due to perceptions of increased donation impact and an effort to maintain the resulting positive mood.GraphTable 2. Summary of Study Results. 2 Notes: Standard deviations are in parentheses. The contrasts are identified with superscript notation: ap ≤.05, bp ≤.01, cp >.1, dp ≤.1. Implications for PracticeOur work was motivated by the documented fact that charitable organizations often struggle to find effective ways to engage donors and solicit donations ([59]). Thus, a central contribution of our research is in confirming that engaging potential donors through creativity can meet this challenge by increasing engagement and enhancing donation behaviors. Substantively, we can recommend incorporating creative activities into fundraising campaigns and charity events as a viable marketing strategy. Indeed, creative activities can be implemented through social media platforms (as exemplified in our pilot study) or in person during charity events and solicitations (as exemplified in Study 2). Current industry practices suggest that some charities have begun testing this approach (i.e., engaging potential donors through creative activities before soliciting them for donations). For example, Roots and Shoots (a charitable organization that supports environmental, conservation, and humanitarian issues) regularly posts a variety of gamified challenges on its website and invites potential donors to participate. Many of these challenges encourage people to incorporate creativity in defining their solutions (e.g., for a ""World Chimpanzee Day Challenge,"" people were asked to design and submit a creative communication graphic to spread awareness about chimpanzee protection).To gain additional insights on practitioners' points of view (concerning our proposed strategy), we sent an email to 220 charities nationwide, inviting them to participate in a short survey. The survey asked three questions that measured the usefulness and applicability of this donation strategy. The first question assessed whether the charity had previously used a creative activity as a preface to a donation request. The second question asked whether, if presented with evidence that engaging donors through a creative activity increases monetary donation, they would implement this strategy in their donation campaigns (1 = ""not very likely,"" and 7 = ""very likely""). The final question assessed how feasible they thought it would be to implement such a strategy (1 = ""not very feasible,"" and 7 = ""very feasible""). We obtained 29 responses from the surveyed national charities (13% response rate). Interestingly, 45% of the charities mentioned that they have previously used a creative activity as a preface to a donation request—showing that our research validates a tactic already in use by some charities today. Most importantly, charities indicated they would definitely be willing to implement this strategy in their donation campaigns (M = 6.55, SD = .69; t(28) = 20.04, p <.001, compared with the midpoint) and thought it would be feasible to implement such a strategy (M = 5.38, SD = 1.68; t(28) = 4.43, p <.001, compared with the midpoint). Though a small sample, these results are encouraging and affirm that utilizing creative activities in charity campaigns is both highly relevant and feasible in the marketplace. Theoretical ContributionsThe current work also provides several theoretical contributions to the field. First, we advance the marketing and charity literature streams by identifying that positive affect experienced during a creative activity is a key mechanism that bolsters subsequent donation behaviors. Second, we offer a deeper understanding of why engaging in a creative activity leads to higher donation behavior through positive affect. Specifically, we show that creative engagement enhances a sense of autonomy, which in turn induces positive affect, which then positively impacts donation behavior. In addition, the relationship between positive affect and enhanced donation behavior is shown to be multiply determined. We identify two specific mechanisms that link affect and behavior: namely, the positive attributions of the impact of one's donation and the mood maintenance tendency of the participant. Third, we establish that the act of creativity itself (not just being primed with creativity as a construct) is a necessary condition to achieve beneficial donation outcomes. Finally, we confirm that the creative activity employed need not relate directly to the organization and/or charitable cause underlying the sought-after donation behavior. This is important both theoretically and practically, as it establishes generalizability in our findings and provides more freedom to charities in defining the type of creativity activity appropriate for their donation campaigns.More broadly, the current research adds to prior work demonstrating the consequences of engaging in creative thinking tasks. While a significant amount of research has been devoted to studying various factors and cognitive processes that impact creativity ([28]; [54]; [55]), much less attention has been paid to the implications and outcomes of being creative ([27]; [71]). Our research shows that there is value in understanding what implications creativity may have for subsequent consumption behaviors. Building up our understanding of the importance of creativity is especially significant in today's consumption environment, where customers are increasingly provided with opportunities to engage in creative activities, from participating in crowdsourcing platforms (e.g., MyStarbucksIdea.com, ideas.lego.com) to engaging in customization processes (e.g., NikeID, Casetify customized phone cases). Limitations and Future ResearchLimitations inherent to our research approach open up several avenues for additional investigation. First, research should be directed toward developing a better understanding of the generalizability of the effects we identify. Although we demonstrated that a creative activity does not have to be specific to the charity in question to provide a positive outcome, we did not assess a broad range of charities and donation appeals. To this end, we conducted a preliminary study examining the impact of the inherent history of the charity (i.e., whether the charity was well-established; for study details; see Web Appendix G) on donation behavior. Here, we found that creative engagement indeed led to enhanced donation behavior, but only when the charity was newly established. When the charity was well-established, the donation behavior was enhanced irrespective of the type of activity utilized in the appeal. Additional research is needed to better explore this potential boundary condition and, more generally, to define other contextual factors that might moderate the effects we have documented here.Second, most of the creative activities tested in this research involved artistry and design (e.g., cookie decorating, T-shirt design, coloring). It remains to be seen whether other forms of creativity could produce similar effects. Indeed, we believe that the effects identified in this research are likely to be observed for any enjoyable creative activities that encourage people to explore and think freely. However, we conjecture that the positive effects defined herein may be attenuated if the creative activity is more convergent in nature (e.g., identifying the one right solution). While we did not directly test the effect of a convergent creative activity on donation behavior, prior research has found that engaging in convergent creative tasks may not lead to a positive affective state ([ 9]). Future research should explore this possibility and outline the breadth of creative activities that are effective precursors to enhanced donation behaviors.Another interesting research question arising from our work concerns the identified difference between creative engagement and creative priming (on subsequent behaviors). We found that engaging in creative activities leads to higher donation behavior, but exposure to creative stimuli does not have a parallel effect. Previous research ([27]), however, has shown that creative priming can influence cognitive processes. Thus, it is important to further distinguish between creative engagement and exposure to creative materials and understand how they differentially impact subsequent behaviors. While both creative engagement and creative priming may influence cognition, perhaps only creative engagement can induce a positive affective state. Future research could further clarify the differences between these stimuli.Finally, future research should continue to build understanding as to when creativity leads to positive (vs. negative) outcomes. Indeed, prior research has shown both positive and negative implications for creative thinking. For example, creativity has been shown to help overcome the burden of secrecy ([29]) and to enhance one's tendency to take a target's perspective ([77]). On the negative side, previous research has found that creativity can lead to dishonesty (e.g., [27]) and enhance unhealthy choices ([31]). We find an opportunity for future work in building on these initial studies to better understand where creativity can influence downstream consumption behaviors. Indeed, we hope that future research will expand on our findings and further investigate the outcomes of creativity for individuals, charities, nonprofit organizations, and the broader marketplace at hand. " 39,Machine Learning for Creativity: Using Similarity Networks to Design Better Crowdfunding Projects," A fundamental tension exists in creativity between novelty and similarity. This research exploits this tension to help creators craft successful projects in crowdfunding. To do so, the authors apply the concept of combinatorial creativity, analyzing each new project in connection to prior similar projects. By using machine learning techniques (Word2vec and Word Mover's Distance), they measure the degrees of similarity between crowdfunding projects on Kickstarter. They analyze how this similarity pattern relates to a project's funding performance and find that ( 1) the prior level of success of similar projects strongly predicts a new project's funding performance, ( 2) the funding performance increases with a balance between being novel and imitative, ( 3) the optimal funding goal is close to the funds raised by prior similar projects, and ( 4) the funding performance increases with a balance between atypical and conventional imitation. The authors use these findings to generate actionable recommendations for project creators and crowdfunding platforms.","Crowdfunding has grown rapidly and become an important source of capital. In 2014, start-up investment generated through crowdfunding was almost half of investment from venture capital ($16 billion vs. $30 billion; see [ 4]). In addition to its fundraising capability, crowdfunding provides useful marketing opportunities. First, project creators can use crowdfunding platforms to advertise ideas and build a reputation ([ 9]). Second, firms can use crowdfunding sites to test the market reaction to a new project ([31]; [40]).Despite the importance of crowdfunding in investment and marketing, creating successful crowdfunding projects remains a major challenge. The top crowdfunding website, Kickstarter, applies an all-or-nothing policy, whereby a project creator collects funds only if the project's funding is successful (i.e., the raised fund pass the funding goal). On Kickstarter, only about 30% of submitted projects end up being successfully funded.[ 7] Further, about 67% of these successful projects raised no more than $10,000 (Kickstarter [20]). To help project creators, previous studies have explored various drivers of success in crowdfunding (see Table 1).GraphTable 1. Current Study and Literature on Crowdfunding. 1 Notes: In terms of content analysis, [27] uses the number of spelling errors in project description; [32] use human coders to label signals in idea pitches. For studies on peer-to-peer lending, which is sometimes recognized as a form of crowdfunding, see [43], [23], and [28].One promising yet unexplored area is the similarity pattern among projects. The prior similar projects of a new project can hold important clues for the new project's funding outcome. This thought has its root in the theory of combinatorial creativity, which views every new idea as some recombination of existing ideas (e.g., [29]; [39]). For predicting success, this theory provides a novel perspective to the current literature on crowdfunding. We may evaluate a new project directly using the level of success of its prior similar projects (given that we have a way to measure the similarity between projects). It requires us to neither specify the exact factors underlying the success of projects nor quantify the effects of these factors on success. Although this approach is intuitively appealing from both a theoretical and method point of view, it has never been applied in crowdfunding (or many other contexts in marketing).In addition to predicting success, measuring the similarity pattern among projects enables us to characterize and examine projects in ways novel to the crowdfunding literature. First, we can measure the degree of novelty of a project and then investigate whether novelty is rewarded or penalized; whether repeated imitation of an idea devalues the idea; and, if so, at what point this devaluation starts. Second, we can measure how much a project's funding goal ""overshoots"" the amount of funds that were raised by prior similar projects and examine whether this overshooting (or undershooting) benefits fundraising. Third, we can measure styles of imitation (e.g., does the new project strictly follow a stereotype or also reach out for atypical elements from other types of projects?). Overall, the examination of these similarity-based characteristics can deliver useful insights for designing new projects.Specifically, this study aims to answer the following research questions: How can we measure the degrees of similarity between all the projects on a crowdfunding site in an objective and automated way? How does the similarity pattern relate to funding performance? Specifically, to what extent… … does the success of prior similar projects predict a new project's success? … is novelty rewarded or penalized? … is it better to let the funding goal overshoot or undershoot the funds raised by prior projects? … does atypicality benefit or hurt funding performance? How can the platform use the similarity pattern to provide creators concrete guidance to design better projects?To answer these questions, we collect data on 98,058 Kickstarter projects from 2009 to 2017 in the three largest categories: Film & Video, Music, and Publishing. We measure the semantic similarity between the descriptions of any two projects by applying two recently developed machine learning techniques, Word2vec ([25]) and Word Mover's Distance (WMD; [22]). We calculate the ""effort"" that one must incur to move the words of one document to the words of the other document. The smaller this effort is, the more similar the two documents are.To operationalize the similarity pattern between projects, we represent it with a similarity network. The nodes represent projects. The strength of a link ( 1) increases with the degree of similarity and ( 2) decreases with the time lapse between two projects. When predicting project j, we focus on all the projects prior to j, with each prior project weighted by its link strength with j. Conceptually, the funding outcome of a project reveals the investor preference for this project. However, because investor preferences change over both time and projects, not every prior project is equally relevant for evaluating the investor preference for a given new project. In this regard, the similarity network offers a way to select the most relevant prior projects and thus provides useful information for predicting success.We examine funding performance from two aspects: whether the funding is successful and how much money is raised. There are several novel findings. First, the average level of success by prior projects, weighted by their links to the focal project, is a significant predictor of the focal project's funding performance. This result holds after we control for the project creator's prior success, the project's funding goal, description length, and the presence of images and videos in the description. Overall, the similarity network is an information source to significantly improve the out-of-sample prediction for funding success.Second, a project's funding performance exhibits an inverted U-shaped relation with the novelty of the project. Here, we measure the novelty of a project via the sum of its links with all prior projects. A larger sum means a greater amount of total similarity between the project and its prior projects, indicating a lower level of novelty (or a higher level of imitativeness). This inverted U-shaped relation can be surprising yet is intuitive. It suggests that successful projects tend to strike a balance between ( 1) being novel and ( 2) appearing familiar to investors.Third, it is optimal to set the funding goal close to the amount of funds raised by prior similar projects. Specifically, we define goal overshoot as the focal project's log funding goal minus the average log funds raised by prior projects, weighted by their links to the focal project. In other words, goal overshoot compares a project's funding goal against a benchmark set by prior similar projects. We find that setting a goal either too low or too high compared with the benchmark decreases the funds to be raised (i.e., an inverted U-shaped effect). Setting a goal lower than the benchmark has a limited effect on the probability of success; setting a goal too much higher than the benchmark decreases the probability of success.Fourth, a project is more likely to succeed when it grounds itself in a stream of closely linked projects yet simultaneously borrows from some projects outside this stream. Under combinatorial creativity, this outside-the-stream imitation constitutes a nonconventional or ""atypical"" use of prior ideas (an example of atypical imitation outside the crowdfunding context is when an article in marketing cites research from a largely unrelated discipline such as physics or biology). We find an inverted U-shaped relation between atypicality and funding performance; neither too little nor too much atypicality benefits fundraising.Drawing on these findings, we devise two recommendation tools that the platform may use to help creators improve projects. Our first recommendation tool helps to set funding goals. Choosing the goal is an important decision for project creators, which matters greatly for funding outcomes ([27]). However, research has provided little concrete guidance on setting the goal optimally. Our results enable us to benchmark a project's goal against the funds raised by prior similar projects. For many projects, we find that the goals were far from optimal, in which case we recommend a ±10% goal adjustment to improve expected funding outcomes. Our second recommendation tool helps creators improve project content. Specifically, we recommend a prior project for the creator to imitate. The recommendations are customized for individual projects to increase each project's chance of success.The crowdfunding literature has focused on several aspects of crowdfunding as endpoints, including the final success of funding campaign (which is also a focus of this research), the dynamics during fundraising period, and investor decisions. The literature also has focused on a variety of sources of explanatory variables for these endpoints, including the project content (as in this article), creator characteristics, and investor behaviors. Table 1 provides a summary. Our study contributes to the literature in several important aspects. First, we apply combinatorial creativity in crowdfunding, using machine learning to construct a similarity network among projects. Second, we significantly improve the out-of-sample prediction of funding success. Third, we derive novel insights on how the similarity pattern with prior projects affects a project's funding performance. From a methodological point of view, the machine-learned similarity network provides important advantages. Previous studies look for explanatory criteria based on specific predefined factors, such as quality signals ([ 3]; [27]) and rhetorical signals ([32]). In contrast, our similarity-based criteria do not restrict attention to specific factors. In addition, we base the similarity measures on the contents of project descriptions, which are particularly important in crowdfunding because investors make decisions on the basis of these descriptions. As a result, our models gain insight, explanation, and prediction.This study also contributes to the broader literature on ideation examined under various contexts aside from crowdfunding, such as crowdsourcing, consumer product development, and the music market. Table 2 provides a summary. These studies have analyzed several sources of explanation for idea success, including the templates of innovation, problem decomposition, creator's social network, idea prototypicality, or genre divergence. Compared with these studies, the current study is unique in its focus on crowdfunding, a source of explanation based on the similarity pattern between ideas, novel findings, and the massive number of ideas examined.GraphTable 2. Current Study and Literature on Ideation. The next sections describe the concept of combinatorial creativity, the research design, the results, and the managerial implications. We conclude with a discussion of the findings and possible extensions of the research. Concept of Combinatorial CreativityBefore we describe the details of our study, we briefly overview the concept of combinatorial creativity, which forms the conceptual basis for our analysis. Previous studies on idea generation have addressed how creativity is generated from a cognitive standpoint. A general descriptive framework is the ""Geneplore"" model proposed by [14]. Geneplore is a portmanteau of the words ""generate"" and ""explore,"" signifying that the development of creative ideas is an iterative interaction of two processes: the generation process and exploration process. In the generation process, people retrieve various pieces of information based on prior knowledge. Then, they create seeds of ideas, called preinventive forms, by recombining those retrieved components. In the exploration process, the preinvented forms can be focused, expanded, or evaluated in further depth. After going through these two processes iteratively, people finally come up with creative ideas.Underlying the Geneplore model is the combinatorial nature of creativity, which sees a new idea as a recombination of existing knowledge. [29], p. 66) conceptualizes economic development as ""the carrying out of new combinations."" [39], p. 331) theorizes that ""knowledge can build upon itself in a combinatoric feedback process."" Although the concept of combinatorial creativity has a long history in the literature of innovation and growth, researchers have only recently begun to gather empirical evidence and applications on this topic. A most representative work is [36]. The authors examine the impact of a piece of scientific research in relation to its bibliography. They show that scientific research tends to have a higher impact when it balances the uses between atypical knowledge and conventional knowledge. Later studies attempt to expand the application of combinatorial creativity in different contexts such as patents ([41]), idea competition ([33]; [35]), and motion pictures ([38]).Compared with the aforementioned studies, ours is unique in several ways. First, this research is the first to apply the concept of combinatorial creativity in the context of crowdfunding. Second, we construct the pairwise connections between ideas on the basis of direct comparison between the content of ideas. In contrast, [36] use the citation network between papers, [41] use the classification by the U.S. Patent and Trademark Office, [33] use the communication network among ideators, [35] examine how an idea deviates from the average, and [38] infers the similarity between movies indirectly from consumer revealed preferences. Third, previous studies mostly focus on the similarity or connection pattern itself. We devote attention to the interactions between the similarity pattern and idea attributes. For example, we examine the extent to which a project's funding goal ""overshoots"" the typical level of funds raised by prior similar projects. Research DesignThis section describes the data, construction of the similarity network, network-based metrics for predicting funding outcomes, control variables, and models.Before we give details on these components of our analysis, it is useful to discuss the conceptual role of the similarity network, as it is the core of our research design. When we try to predict the funding outcomes for a new project, the key determinant is investors' preferences—what types of projects they find worth supporting, what elements of ideas they find appealing, and so on. In this regard, the funding outcomes of historical projects provide a database containing many instances of revelation of investors' preferences (where each historical project constitutes one instance). However, the crucial question is which part of this database we should use when predicting the funding performance of a given new project. An intuitive answer is not difficult. For example, if the new project aims to develop a video about motorcycle ride trips, then the success or failure of a prior film project on a family drama is unlikely to offer relevant information. However, the funding outcomes of prior projects on traveling or motorcycles should seem relevant. In addition, we probably should give more weight to more recent projects. Projects completed ten years ago, even if their content is similar to the new project's, may no longer be relevant because investor preferences may have significantly changed since then.We use the similarity network to operationalize the aforementioned intuition. We let each link in the network measure the degree of similarity (computed using machine learning methods) as well as the time proximity between a project pair. Then, when predicting the funding outcome for a focal project, we weigh each prior project by its link strength with the focal project. In addition to selecting relevant prior projects for prediction, the network allows us to characterize a new project in relation to prior projects (e.g., novelty of the new project). These characteristics may affect the investors' preferences and thus funding outcomes. We define and discuss the metrics for these characteristics subsequently in this section. DataWe collect data from Kickstarter, one of the largest reward-based crowdfunding platforms. We acquire the information of each project from May 2009 to the end of 2017.We focus on English-language projects in the United States. We also focus on the projects belonging to the top three largest project categories: Film & Video, Music, and Publishing. To adjust for inflation over time, we normalize all the monetary values (e.g., funding goal and funds raised by projects) to 2017 dollars using the Consumer Price Index. Following [27] procedure, we eliminate the projects with outlying project goals (i.e., goals smaller than $100 or larger than $1,000,000 [1.24% of data]). Furthermore, we do not consider the projects with fewer than 50 words in the description text (2.5% of data), in order to correctly measure the content similarity between projects. In the end, our data include 98,058 projects on Kickstarter (Film & Video: 37,641 projects, Music: 35,943 projects, and Publishing: 24,474 projects).We focus on two measures for the outcome of a project: ( 1) funding success and ( 2) funds raised. Funding success is a binary variable. A project is classified as a success if it reaches the preset funding goal before the project campaign ends (project creators set the campaign lengths of their projects, the vast majority of which are either 30 or 60 days [the maximum length allowed is 60 days]). ""Funds raised"" denotes the total amount of money that the project collects at the end of the project campaign, whether it exceeds the funding goal or not. Note that under Kickstarter's all-or-nothing policy, a project cannot collect the funds unless the funding goal is successfully reached. Nevertheless, both funding success and funds raised are important outcomes reflecting the investors' interests and confidence in the project.The average rate of funding success over our entire data from 2009 to 2017 is about.46. The average success rate is higher in the Music category than the other two categories (Film & Video:.43, Music:.56, Publishing:.34) and has persisted over time. This difference in the success rate is partly explained by Music projects tending to ask for lower funding goals (median goal in each category: Film & Video: $7,500, Music: $4,359, Publishing: $5,107). Construction of Similarity NetworkTo measure the similarity between projects semantically, we apply machine learning techniques on the descriptive texts of crowdfunding projects. Note that our network is constructed to represent the similarity relations between projects (i.e., nodes are projects). This network is different from, for example, the semantic network between words in [35]. The method we adopt to measure similarity between projects is called WMD, which is based on Word2vec. We describe this method next. Alternatively, one could measure similarity with latent Dirichlet allocation (LDA; [ 7]) that factors text documents into topics. However, WMD provides a better prediction performance than LDA in our application (see Web Appendix A). WMD also has some practical advantages for our application, as we discuss subsequently. Word-level similarity (Word2vec)We apply Word2vec ([25]; [26]) to measure the similarity between the words in project descriptions. Word2vec is a machine learning algorithm designed to learn the semantic relations between words. Specifically, it applies a two-layer neural network to convert words into high-dimensional vectors. Taking a large corpus of text as an input, the model generates a vector to represent each word in the corpus. Word2vec positions each word in the vector space such that words that share common contexts in the corpus are located close by. Word2vec is also capable of capturing many semantic relations with vector operations. For example, the vector representing ""King"" minus the vector for ""Kings"" is equal to the vector for ""Queen"" minus the vector for ""Queens.""One implementation of Word2vec is known as the skip-gram model. It uses the two-layer neural network to predict the surrounding words when a central word is given. Google adopts this implementation to provide a pretrained Word2vec using the Google News corpus. The vocabulary contains more than three million unique words or phrases, each of which is presented by a 300-dimensional vector ([25]). We use this Word2vec trained by Google in this study. Alternatively, one can train context-specific Word2vec on the crowdfunding project descriptions. However, using Google's Word2vec here allows for easier implementation, especially for the platform, as we discuss next. Document-level similarity (WMD)A simple way to measure the similarity between two documents is to count the overlapping words that appear in both documents. However, two similar documents do not need to share even a single word (consider, e.g., ""President speaks on immigration"" and ""Trump talks about borders""). Therefore, we want to account for the similarities between words.We apply a recently developed method of measuring document similarity called WMD ([22]), which is built on Word2vec. Specifically, the method regards a document as the collection of word vectors as prescribed by Word2vec and minimizes the total travel distance of moving all word vectors in one document to all word vectors in another document. The minimized travel distance is used as a measure of dissimilarity between the two documents. WMD has been shown to perform better than previous methods in measuring document similarity (see Web Appendix A).The application of WMD with Google's pretrained Word2vec makes the computation of similarity between two crowdfunding projects a truly pairwise operation independent of other crowdfunding projects. This feature is different from the LDA and related text analysis models (e.g., latent semantic indexing; [12]), which require training first on the entire corpus to extract latent topics. As new projects are posted to the crowdfunding website every day, the corpus changes over time, which would require retraining the text model every once in a while. The WMD, coupled with Google's Word2vec, does not require such retraining and thus should be easier to implement for the platform.Before moving on, we briefly describe some summary statistics to help explain our implementation. Take the Publishing category as an example. There are 24,474 projects in this category. We take the text in the description (including the blurb) of each project. After removing stop words (e.g., ""the,"" ""a""), rare words, and words not in Google's Word2vec vocabulary, there are 19,032 unique words that we use in computing the WMDs between projects.[ 8] An average project has 209.64 unique words (SD = 161.73). There are 299,476,101 unique pairs ( = 24,474 × (24,474–1)/2) of projects in this category. A WMD is computed for each pair. We do not consider similarities between projects of different categories (e.g., a Publishing project and a Music project). Accounting for cross-category similarities entails a much larger computational cost, but it may improve predictions and insights. This is an interesting topic for future research.For each category, the distribution of the WMDs is bell-shaped and mostly symmetrical. The distributions of the three categories are very similar (Film & Video: M = 3.10, SD = .18; Music: M = 3.01, SD = .20; Publishing: M = 3.12, SD = .18). We note that the mean WMD in the Music category is slightly smaller than the other two categories. We think this difference may be because films and books tend to involve lengthier and more explicit storytelling, which allows for more ways to differentiate projects. Similarity network between projectsNetworks are generally designed to keep track of pairwise relations between individuals. We use a network to present the similarity relations between projects in a given crowdfunding category. Each node represents a project. An unweighted network can be constructed by placing a link between two projects if and only if their similarity passes a threshold. Such an unweighted network is actually sufficient for deriving the main qualitative results in this article. However, we can achieve better prediction performance by constructing a weighted network, where every pairwise relation takes a continuous value wij≥0 . We call this value the link strength between node i and j.As discussed, we consider two properties when assigning the value for wij : ( 1) the value of wij should increase with the similarity between the contents of i and j, and ( 2) the value of wij should decrease with the time gap between i and j. As discussed, the second property is to account for the fact that investor tastes may be time-varying. Together, the two properties enable us to focus on the projects that are more similar and more recent to the focal project when predicting the funding outcomes for the focal project.Specifically, let dij denote the WMD between any two projects i and j. Let ti denote the starting date of any project i. Let 0<δ≤1 be a decay factor. We specify that, for any i and j in the same category such that i≠j , wij=δ|ti−tj|×L(γ0−γ1dij). Graph( 1)In Equation 1, L is the logistic function. We choose the exact values of γ0 , γ1 , and δ using a 10-fold cross-validation (5-fold and 15-fold cross-validation give very similar values). The logistic function leads to a significantly better prediction performance for funding success than some of the other functions of dij , such as 1/dij , 1−dij/max{dij} , or e−γdij . Also note the logistic function becomes a step function when γ0 and γ1 are sufficiently large, so the specification in Equation 1 contains the unweighted network as a special case. Finally, we note that only the relative link strengths carry a meaning; scaling wij by a common factor for all (i, j) pairs does not affect our subsequent analyses. Network-Based MetricsWe focus on several metrics based on the similarity network to help predict new projects' funding performance. Before we detail each metric, it is useful to set up an illustrative example, which we use throughout the article (see Figure 1). For illustration purposes, we restrict attention to only six projects—the actual similarity network contains tens of thousands of projects for each category. The thickness of a link represents the link strength wij . The horizontal position of a node reflects its starting date ti . Each project also has a completion date Ti . In this example, we treat ti=Ti for simplicity.[ 9]Graph: Figure 1. An illustration of similarity network.GraphTable 3. Selected Details of the Example Projects. The six projects are taken from the Film & Video category in our data (for details, see Table 3). Project 1 is a documentary about a world runner's journey. Project 2 is a comedic portrait of depression issues in modern lives. Project 3 is a documentary of restoring and riding a motorcycle for an Atlanta-Alaska trip. Project 4 is a family drama of dealing with abandonment issues and finding happiness. Project 5 is a pan-American travel journey in an old van. Project 6 is a motorcycle journey throughout Florida. Amount of prior similarityResearch on creativity has long been interested in the role of novelty in new product performance (see, e.g., [15]; [30]). Intuitively, a new idea will be perceived as less novel (or more imitative) when it is very similar to many previous ideas, especially the more recent ideas. The similarity network offers us a unique opportunity to objectively measure an idea's degree of novelty using machine learning techniques.Let ti and Ti denote the starting and completion dates of any project i. For any focal project i, we define the amount of prior similarity as log(1+∑j:Tj0,  qijt≥0.1The utility in Equation 1 is quasiconcave, continuously differentiable, and an increasing function of expenditure qijt .[ 9] To be a valid utility function, we need restrictions on Equation 1 such that ψi0t, ψijt>0 , and γijt>0 . The expenditure on the outside good qi0t is defined as a numeraire that includes the aggregate expenditure on all outside goods (e.g., rent, grocery, gas), but excludes charitable giving to the focal NPO. Individuals consume outside goods in each time period, and the price of the outside good is normalized to unity ( pi0t=1 ). The ""price"" of giving to option j , pijt , is related to an individual's marginal tax rate. [ 8] reports that in two-thirds of countries, including the United States, taxpayers can claim charitable giving as a deduction from taxable income. If the marginal tax rate is 0